Paper Digest: ICASSP 2021 Highlights
Download ICASSP-2021-Paper-Digests.pdf– highlights of all ICASSP-2021 papers. Readers can also choose to read this highlight article on our console, which allows users to filter out papers using keywords. The IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) is one of the top signal processing conferences in the world. In 2021, it is to be held online.
To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.
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TABLE 1: Paper Digest: ICASSP 2021 Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | Rethinking The Separation Layers In Speech Separation Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we empirically examine those questions by designing models with varying configurations in the SIMO and SISO modules. |
Y. Luo; Z. Chen; C. Han; C. Li; T. Zhou; N. Mesgarani; |
| 2 | On Permutation Invariant Training For Speech Source Separation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study permutation invariant training (PIT), which targets at the permutation ambiguity problem for speaker independent source separation models. |
X. Liu; J. Pons; |
| 3 | Count And Separate: Incorporating Speaker Counting For Continuous Speaker Separation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This study leverages frame-wise speaker counting to switch between speech enhancement and speaker separation for continuous speaker separation. |
Z. -Q. Wang; D. Wang; |
| 4 | Ultra-Lightweight Speech Separation Via Group Communication Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide a simple model design paradigm that explicitly designs ultra-lightweight models without sacrificing the performance. |
Y. Luo; C. Han; N. Mesgarani; |
| 5 | Attention Is All You Need In Speech Separation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Transformers are emerging as a natural alternative to standard RNNs, replacing recurrent computations with a multi-head attention mechanism.In this paper, we propose the SepFormer, a novel RNN-free Transformer-based neural network for speech separation. |
C. Subakan; M. Ravanelli; S. Cornell; M. Bronzi; J. Zhong; |
| 6 | Multichannel Overlapping Speaker Segmentation Using Multiple Hypothesis Tracking Of Acoustic And Spatial Features Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we explore the use of a new multimodal approach for overlapping speaker segmentation that tracks both the fundamental frequency (F0) of the speaker and the speaker?s direction of arrival (DOA) simultaneously. |
A. O. T. Hogg; C. Evers; P. A. Naylor; |
| 7 | Semi-Supervised Singing Voice Separation With Noisy Self-Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Given a limited set of labeled data, we present a method to leverage a large volume of unlabeled data to improve the model?s performance. |
Z. Wang; R. Giri; U. Isik; J. -M. Valin; A. Krishnaswamy; |
| 8 | Neuro-Steered Music Source Separation With EEG-Based Auditory Attention Decoding And Contrastive-NMF Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel informed music source separation paradigm, which can be referred to as neuro-steered music source separation. |
G. Cantisani; S. Essid; G. Richard; |
| 9 | Complex Ratio Masking For Singing Voice Separation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a complex ratio masking method for voice and accompaniment separation. |
Y. Zhang; Y. Liu; D. Wang; |
| 10 | Transcription Is All You Need: Learning To Separate Musical Mixtures With Score As Supervision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we use musical scores, which are comparatively easy to obtain, as a weak label for training a source separation system. |
Y. -N. Hung; G. Wichern; J. Le Roux; |
| 11 | All For One And One For All: Improving Music Separation By Bridging Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes several improvements for music separation with deep neural networks (DNNs), namely a multi-domain loss (MDL) and two combination schemes. |
R. Sawata; S. Uhlich; S. Takahashi; Y. Mitsufuji; |
| 12 | An Hrnet-Blstm Model With Two-Stage Training For Singing Melody Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this problem, we propose to use a pitch refinement method to refine the semitone-level pitch sequences decoded from massive melody MIDI files to generate a large number of fundamental frequency (F0) values for model training. |
Y. Gao; X. Du; B. Zhu; X. Sun; W. Li; Z. Ma; |
| 13 | DeepF0: End-To-End Fundamental Frequency Estimation for Music and Speech Signals Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel pitch estimation technique called DeepF0, which leverages the available annotated data to directly learns from the raw audio in a data-driven manner. |
S. Singh; R. Wang; Y. Qiu; |
| 14 | Differentiable Signal Processing With Black-Box Audio Effects Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a data-driven approach to automate audio signal processing by incorporating stateful third-party, audio effects as layers within a deep neural network. |
M. A. Mart�nez Ram�rez; O. Wang; P. Smaragdis; N. J. Bryan; |
| 15 | Automatic Multitrack Mixing With A Differentiable Mixing Console Of Neural Audio Effects Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these challenges, we propose a domain-inspired model with a strong inductive bias for the mixing task. |
C. J. Steinmetz; J. Pons; S. Pascual; J. Serr�; |
| 16 | Sequence-To-Sequence Singing Voice Synthesis With Perceptual Entropy Loss Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a Perceptual Entropy (PE) loss derived from a psycho-acoustic hearing model to regularize the network. |
J. Shi; S. Guo; N. Huo; Y. Zhang; Q. Jin; |
| 17 | Reverb Conversion Of Mixed Vocal Tracks Using An End-To-End Convolutional Deep Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In response, we propose an end-to-end system capable of switching the musical reverb factor of two different mixed vocal tracks. |
J. Koo; S. Paik; K. Lee; |
| 18 | Extending Music Based On Emotion And Tonality Via Generative Adversarial Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a generative model for music extension in this paper. |
B. -W. Tseng; Y. -L. Shen; T. -S. Chi; |
| 19 | Improving The Robustness Of Right Whale Detection In Noisy Conditions Using Denoising Autoencoders And Augmented Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The aim of this paper is to examine denoising autoencoders (DAEs) for improving the detection of right whales recorded in harsh marine environments. |
W. Vickers; B. Milner; R. Lee; |
| 20 | Self-Supervised VQ-VAE for One-Shot Music Style Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we are specifically interested in the problem of one-shot timbre transfer. |
O. C�fka; A. Ozerov; U. Simsekli; G. Richard; |
| 21 | Capturing Temporal Dependencies Through Future Prediction for CNN-Based Audio Classifiers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To capture audio temporal dependencies using CNNs, we take a different approach from the purely architecture-induced method and explicitly encode temporal dependencies into the CNN-based audio classifiers. |
H. Song; J. Han; S. Deng; Z. Du; |
| 22 | Segmental Dtw: A Parallelizable Alternative to Dynamic Time Warping Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we explore parallelizable alternatives to DTW for globally aligning two feature sequences. |
T. Tsai; |
| 23 | Pitch-Timbre Disentanglement Of Musical Instrument Sounds Based On Vae-Based Metric Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper describes a representation learning method for disentangling an arbitrary musical instrument sound into latent pitch and timbre representations. |
K. Tanaka; R. Nishikimi; Y. Bando; K. Yoshii; S. Morishima; |
| 24 | Asynchronous Acoustic Echo Cancellation Over Wireless Channels Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a novel acoustic echo cancellation framework for systems where the loudspeaker and the microphone array are not synchronized. |
R. Ayrapetian; P. Hilmes; M. Mansour; T. Kristjansson; C. Murgia; |
| 25 | Combining Adaptive Filtering And Complex-Valued Deep Postfiltering For Acoustic Echo Cancellation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this contribution, we introduce a novel approach to noise-robust acoustic echo cancellation employing a complex-valued Deep Neural Network (DNN) for postfiltering. |
M. M. Halimeh; T. Haubner; A. Briegleb; A. Schmidt; W. Kellermann; |
| 26 | Deep Residual Echo Suppression With A Tunable Tradeoff Between Signal Distortion And Echo Suppression Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a residual echo suppression method using a UNet neural network that directly maps the outputs of a linear acoustic echo canceler to the desired signal in the spectral domain. |
A. Ivry; I. Cohen; B. Berdugo; |
| 27 | Robust STFT Domain Multi-Channel Acoustic Echo Cancellation with Adaptive Decorrelation of The Reference Signals Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present an algorithm for multi-channel acoustic echo cancellation for a high-fidelity audio reproduction system equipped with a microphone array for voice control. |
S. Bagheri; D. Giacobello; |
| 28 | A Method for Determining Periodically Time-Varying Bias and Its Applications in Acoustic Feedback Cancellation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we make use of that knowledge and propose a method to detect different acoustic situations, based on the level of residual bias. |
M. Guo; |
| 29 | Weighted Recursive Least Square Filter and Neural Network Based Residual ECHO Suppression for The AEC-Challenge Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a real-time Acoustic Echo Cancellation (AEC) algorithm submitted to the AEC-Challenge. |
Z. Wang; Y. Na; Z. Liu; B. Tian; Q. Fu; |
| 30 | ICASSP 2021 Acoustic Echo Cancellation Challenge: Integrated Adaptive Echo Cancellation with Time Alignment and Deep Learning-Based Residual Echo Plus Noise Suppression Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper describes a three-stage acoustic echo cancellation (AEC) and suppression framework for the ICASSP 2021 AEC Challenge. |
R. Peng; L. Cheng; C. Zheng; X. Li; |
| 31 | ICASSP 2021 Acoustic Echo Cancellation Challenge: Datasets, Testing Framework, and Results Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this challenge, we open source two large datasets to train AEC models under both single talk and double talk scenarios. |
K. Sridhar; et al. |
| 32 | AEC in A Netshell: on Target and Topology Choices for FCRN Acoustic Echo Cancellation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we will heal this issue and significantly improve the near-end speech component quality over existing approaches. |
J. Franzen; E. Seidel; T. Fingscheidt; |
| 33 | Kernel-Interpolation-Based Filtered-X Least Mean Square for Spatial Active Noise Control In Time Domain Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Time-domain spatial active noise control (ANC) algorithms based on kernel interpolation of a sound field are proposed. |
J. Brunnstr�m; S. Koyama; |
| 34 | Wave-Domain Optimization of Secondary Source Placement Free From Information of Error Sensor Positions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, a method free from the information of specific error sensors positions is proposed. |
J. Xu; K. Chen; Y. Li; |
| 35 | Lasaft: Latent Source Attentive Frequency Transformation For Conditioned Source Separation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The goal of this paper is to extend the FT block to fit the multi-source task. |
W. Choi; M. Kim; J. Chung; S. Jung; |
| 36 | Surrogate Source Model Learning for Determined Source Separation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose to learn surrogate functions of universal speech priors for determined blind speech separation. |
R. Scheibler; M. Togami; |
| 37 | Auditory Filterbanks Benefit Universal Sound Source Separation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We proposed parameterized Gammatone and Gammachirp filterbanks, which improved performance with fewer parameters and better interpretability. |
H. Li; K. Chen; B. U. Seeber; |
| 38 | What�s All The Fuss About Free Universal Sound Separation Data? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce the Free Universal Sound Separation (FUSS) dataset, a new corpus for experiments in separating mixtures of an unknown number of sounds from an open domain of sound types. |
S. Wisdom; et al. |
| 39 | SepNet: A Deep Separation Matrix Prediction Network for Multichannel Audio Source Separation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose SepNet, a deep neural network (DNN) designed to predict separation matrices from multichannel observations. |
S. Inoue; H. Kameoka; L. Li; S. Makino; |
| 40 | CDPAM: Contrastive Learning for Perceptual Audio Similarity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces CDPAM ?a metric that builds on and advances DPAM. |
P. Manocha; Z. Jin; R. Zhang; A. Finkelstein; |
| 41 | Linear Multichannel Blind Source Separation Based on Time-Frequency Mask Obtained By Harmonic/Percussive Sound Separation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Building up on this framework, in this paper, we propose a unification of determined BSS and harmonic/percussive sound separation (HPSS). |
S. Oyabu; D. Kitamura; K. Yatabe; |
| 42 | Multichannel-based Learning for Audio Object Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we propose a novel deep learning approach to object extraction that learns from the multichannel renders of object-based productions, instead of directly learning from the audio objects themselves. |
D. Arteaga; J. Pons; |
| 43 | DBnet: Doa-Driven Beamforming Network for End-to-end Reverberant Sound Source Separation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we propose a direction-of-arrival-driven beamforming network (DBnet) consisting of direction-of-arrival (DOA) estimation and beamforming layers for end-to-end source separation. |
A. Aroudi; S. Braun; |
| 44 | Joint Dereverberation and Separation With Iterative Source Steering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new algorithm for joint dereverberation and blind source separation (DR-BSS). |
T. Nakashima; R. Scheibler; M. Togami; N. Ono; |
| 45 | Exploiting Non-Negative Matrix Factorization for Binaural Sound Localization in The Presence of Directional Interference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This study presents a novel solution to the problem of binaural localization of a speaker in the presence of interfering directional noise and reverberation. |
I. �rnolfsson; T. Dau; N. Ma; T. May; |
| 46 | Blind Extraction of Moving Audio Source in A Challenging Environment Supported By Speaker Identification Via X-Vectors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel approach for semi-supervised extraction of a moving audio source of interest (SOI) applicable in reverberant and noisy environments. |
J. Malek; J. Jansky; T. Kounovsky; Z. Koldovsky; J. Zdansky; |
| 47 | Mind The Beat: Detecting Audio Onsets from EEG Recordings of Music Listening Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a deep learning approach to predicting audio event onsets in electroencephalogram (EEG) recorded from users as they listen to music. |
A. Vinay; A. Lerch; G. Leslie; |
| 48 | Don�t Look Back: An Online Beat Tracking Method Using RNN and Enhanced Particle Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Don’t Look back! (DLB), a novel approach optimized for efficiency when performing OBT. |
M. Heydari; Z. Duan; |
| 49 | Singing Melody Extraction from Polyphonic Music Based on Spectral Correlation Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore the idea of modeling spectral correlation explicitly for melody extraction. |
X. Du; B. Zhu; Q. Kong; Z. Ma; |
| 50 | Improving Automatic Drum Transcription Using Large-Scale Audio-to-Midi Aligned Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this issue, we propose a semi-automatic way of compiling a labeled dataset using the audio-to-MIDI alignment technique. |
I. -C. Wei; C. -W. Wu; L. Su; |
| 51 | Frequency-Temporal Attention Network for Singing Melody Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by these intrinsic characteristics, a frequency-temporal attention network is proposed to mimic human auditory for singing melody extraction. |
S. Yu; X. Sun; Y. Yu; W. Li; |
| 52 | Statistical Correction of Transcribed Melody Notes Based on Probabilistic Integration of A Music Language Model and A Transcription Error Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper describes a statistical post-processing method for automatic singing transcription that corrects pitch and rhythm errors included in a transcribed note sequence. |
Y. Hiramatsu; G. Shibata; R. Nishikimi; E. Nakamura; K. Yoshii; |
| 53 | Reliability Assessment of Singing Voice F0-Estimates Using Multiple Algorithms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we consider an approach to automatically assess the reliability of F0-trajectories estimated from monophonic singing voice recordings. |
S. Rosenzweig; F. Scherbaum; M. M�ller; |
| 54 | End-to-End Lyrics Recognition with Voice to Singing Style Transfer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a data augmentation method that converts natural speech to singing voice based on vocoder based speech synthesizer. |
S. Basak; S. Agarwal; S. Ganapathy; N. Takahashi; |
| 55 | Singing Language Identification Using A Deep Phonotactic Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents a modernized phonotactic system for SLID on polyphonic music: phoneme recognition is performed with a Connectionist Temporal Classification (CTC)-based acoustic model trained with multilingual data, before language classification with a recurrent model based on the phonemes estimation. |
L. Renault; A. Vaglio; R. Hennequin; |
| 56 | On The Preparation and Validation of A Large-Scale Dataset of Singing Transcription Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a large-scale dataset for singing transcription, along with some methods for fine-tuning and validating its contents. |
J. -Y. Wang; J. -S. R. Jang; |
| 57 | Joint Multi-Pitch Detection and Score Transcription for Polyphonic Piano Music Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a method for joint multi-pitch detection and score transcription for polyphonic piano music. |
L. Liu; V. Morfi; E. Benetos; |
| 58 | Karaoke Key Recommendation Via Personalized Competence-Based Rating Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address a novel task of recommending a suitable key for a user to sing a given song to meet his or her vocal competence, by proposing the Personalized Competence-based Rating Prediction (PCRP) model. |
Y. Wang; S. Tanaka; K. Yokoyama; H. -T. Wu; Y. Fang; |
| 59 | A Closed-Loop Gain-Control Feedback Model for The Medial Efferent System of The Descending Auditory Pathway Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We have implemented a dynamic, closed-loop gain-control system into an existing auditory model to simulate parts of the efferent system. |
A. Farhadi; S. G. Jennings; E. A. Strickland; L. H. Carney; |
| 60 | DHASP: Differentiable Hearing Aid Speech Processing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore an alternative approach to finding the optimal fitting by introducing a hearing aid speech processing framework, in which the fitting is optimised in an automated way using an intelligibility objective function based on the HASPI physiological auditory model. |
Z. Tu; N. Ma; J. Barker; |
| 61 | Computationally Efficient DNN-Based Approximation of An Auditory Model for Applications in Speech Processing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Hence, in this work we propose and evaluate DNN-based approximations of a state-of-the-art auditory model. |
A. Nagathil; F. G�bel; A. Nelus; I. C. Bruce; |
| 62 | Cascaded All-Pass Filters with Randomized Center Frequencies and Phase Polarity for Acoustic and Speech Measurement and Data Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a new member of TSP (Time Stretched Pulse) for acoustic and speech measurement infrastructure, based on a simple all-pass filter and systematic randomization. |
H. Kawahara; K. Yatabe; |
| 63 | Probing Acoustic Representations for Phonetic Properties Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We compare features from two conventional and four pre-trained systems in some simple frame-level phonetic classification tasks, with classifiers trained on features from one version of the TIMIT dataset and tested on features from another. |
D. Ma; N. Ryant; M. Liberman; |
| 64 | An End-To-End Non-Intrusive Model for Subjective and Objective Real-World Speech Assessment Using A Multi-Task Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel multi-task non-intrusive approach that is capable of simultaneously estimating both subjective and objective scores of real-world speech, to help facilitate learning. |
Z. Zhang; P. Vyas; X. Dong; D. S. Williamson; |
| 65 | Few-Shot Continual Learning for Audio Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a few-shot continual learning framework for audio classification, where we can continuously expand a trained base classifier to recognize novel classes based on only few labeled data at inference time. |
Y. Wang; N. J. Bryan; M. Cartwright; J. Pablo Bello; J. Salamon; |
| 66 | Zero-Shot Audio Classification with Factored Linear and Nonlinear Acoustic-Semantic Projections Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study zero-shot learning in audio classification through factored linear and nonlinear acoustic-semantic projections between audio instances and sound classes. |
H. Xie; O. R�s�nen; T. Virtanen; |
| 67 | Unsupervised and Semi-Supervised Few-Shot Acoustic Event Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we study unsupervised and semi-supervised learning approaches for few-shot AEC. |
H. -P. Huang; K. C. Puvvada; M. Sun; C. Wang; |
| 68 | Flow-Based Self-Supervised Density Estimation for Anomalous Sound Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To develop a machine sound monitoring system, a method for detecting anomalous sound is proposed. |
K. Dohi; T. Endo; H. Purohit; R. Tanabe; Y. Kawaguchi; |
| 69 | Self-Training for Sound Event Detection in Audio Mixtures Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to address limitations in availability of training data, this work proposes a self-training technique to leverage unlabeled datasets in supervised learning using pseudo label estimation. |
S. Park; A. Bellur; D. K. Han; M. Elhilali; |
| 70 | Prototypical Networks for Domain Adaptation in Acoustic Scene Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In the search for an optimal solution to the said problem, we explore a metric learning approach called prototypical networks using the TUT Urban Acoustic Scenes dataset, which consists of 10 different acoustic scenes recorded across 10 cities. |
S. Singh; H. L. Bear; E. Benetos; |
| 71 | A Global-Local Attention Framework for Weakly Labelled Audio Tagging Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a novel two-stream framework for audio tagging by exploiting the global and local information of sound events. |
H. Wang; Y. Zou; W. Wang; |
| 72 | An Improved Mean Teacher Based Method for Large Scale Weakly Labeled Semi-Supervised Sound Event Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents an improved mean teacher (MT) based method for large-scale weakly labeled semi-supervised sound event detection (SED), by focusing on learning a better student model. |
X. Zheng; Y. Song; I. McLoughlin; L. Liu; L. -R. Dai; |
| 73 | Comparison of Deep Co-Training and Mean-Teacher Approaches for Semi-Supervised Audio Tagging Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we adapted the Deep-Co-Training algorithm (DCT) to perform AT, and compared it to another SSL approach called Mean Teacher (MT), that has been used by the winning participants of the DCASE competitions these last two years. |
L. Cances; T. Pellegrini; |
| 74 | The Benefit of Temporally-Strong Labels in Audio Event Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To reveal the importance of temporal precision in ground truth audio event labels, we collected precise (~0.1 sec resolution) strong labels for a portion of the AudioSet dataset. |
S. Hershey; et al. |
| 75 | Unsupervised Contrastive Learning of Sound Event Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore unsupervised contrastive learning as a way to learn sound event representations. |
E. Fonseca; D. Ortego; K. McGuinness; N. E. O�Connor; X. Serra; |
| 76 | Sound Event Detection By Consistency Training and Pseudo-Labeling With Feature-Pyramid Convolutional Recurrent Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To exploit large amount of unlabeled in-domain data efficiently, we applied three semi-supervised learning strategies: interpolation consistency training (ICT), shift consistency training (SCT), and weakly pseudo-labeling. |
C. -Y. Koh; Y. -S. Chen; Y. -W. Liu; M. R. Bai; |
| 77 | SESQA: Semi-Supervised Learning for Speech Quality Assessment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we tackle these problems with a semi-supervised learning approach, combining available annotations with programmatically generated data, and using 3 different optimization criteria together with 5 complementary auxiliary tasks. |
J. Serr�; J. Pons; S. Pascual; |
| 78 | Detecting Signal Corruptions in Voice Recordings For Speech Therapy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this article we design an experimental setup to detect disturbances in voice recordings, such as additive noise, clipping, infrasound and random muting. |
H. Nyl�n; S. Chatterjee; S. Ternstr�m; |
| 79 | MBNET: MOS Prediction for Synthesized Speech with Mean-Bias Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose MBNet, a MOS predictor with a mean subnet and a bias subnet to better utilize every judge score in MOS datasets, where the mean subnet is used to predict the mean score of each utterance similar to that in previous works, and the bias subnet to predict the bias score (the difference between the mean score and each individual judge score) and capture the personal preference of individual judges. |
Y. Leng; X. Tan; S. Zhao; F. Soong; X. -Y. Li; T. Qin; |
| 80 | Non-Intrusive Binaural Prediction of Speech Intelligibility Based on Phoneme Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we explore an approach for modeling speech intelligibility in spatial acoustic scenes. |
J. Ro�bach; S. R�ttges; C. F. Hauth; T. Brand; B. T. Meyer; |
| 81 | Warp-Q: Quality Prediction for Generative Neural Speech Codecs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present WARP-Q, a full-reference objective speech quality metric that uses dynamic time warping cost for MFCC speech representations. |
W. A. Jassim; J. Skoglund; M. Chinen; A. Hines; |
| 82 | Crowdsourcing Approach for Subjective Evaluation of Echo Impairment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We then introduce an open-source crowdsourcing approach for subjective evaluation of echo impairment which can be used to evaluate the performance of AECs. |
R. Cutler; B. Nadari; M. Loide; S. Sootla; A. Saabas; |
| 83 | Amplitude Matching: Majorization�Minimization Algorithm for Sound Field Control Only with Amplitude Constraint Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: A sound field control method for synthesizing a desired amplitude distribution inside a target region, amplitude matching, is proposed. |
S. Koyama; T. Amakasu; N. Ueno; H. Saruwatari; |
| 84 | 3D Multizone Soundfield Reproduction in A Reverberant Environment Using Intensity Matching Method Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address this challenge and propose a multizone reproduction method for 3D soundfield in a reverberant room based on intensity matching. |
H. Zuo; T. D. Abhayapala; P. N. Samarasinghe; |
| 85 | The Far-Field Equatorial Array for Binaural Rendering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a method for obtaining a spherical harmonic representation of a sound field based on a microphone array along the equator of a rigid spherical scatterer. |
J. Ahrens; H. Helmholz; D. L. Alon; S. V. A. Gar�; |
| 86 | Spherical Harmonic Representation for Dynamic Sound-Field Measurements Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a new physical interpretation of the dynamic sampling problem. |
F. Katzberg; M. Maass; A. Mertins; |
| 87 | Direction Preserving Wind Noise Reduction Of B-Format Signals Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, methods to reduce wind noise while limiting the spatial distortions of the original signal are proposed based on recent works of the present authors. |
A. Herzog; D. Mirabilii; E. A. P. Habets; |
| 88 | Refinement of Direction of Arrival Estimators By Majorization-Minimization Optimization on The Array Manifold Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Unlike most conventional methods that rely exclusively on grid search, we introduce a continuous optimization algorithm to refine DOA estimates beyond the resolution of the initial grid. |
R. Scheibler; M. Togami; |
| 89 | On The Predictability of Hrtfs from Ear Shapes Using Deep Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Using 3D ear shapes as inputs, we explore the bounds of HRTF predictability using deep neural networks. |
Y. Zhou; H. Jiang; V. K. Ithapu; |
| 90 | Applied Methods for Sparse Sampling of Head-Related Transfer Functions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper describes the application of two methods for ear-aligned HRTF interpolation by sparse sampling: Orthogonal Matching Pursuit and Principal Component Analysis. |
L. Arbel; Z. Ben-Hur; D. L. Alon; B. Rafaely; |
| 91 | Personalized HRTF Modeling Using DNN-Augmented BEM Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new deep learning method that combines measurements and numerical simulations to take the best of three worlds. |
M. Zhang; J. -H. Wang; D. L. James; |
| 92 | Efficient Training Data Generation for Phase-Based DOA Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a low complexity online data generation method to train DL models with a phase-based feature input. |
F. H�bner; W. Mack; E. A. P. Habets; |
| 93 | Acoustic Reflectors Localization from Stereo Recordings Using Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a fully convolutional network (FCN) that localizes reflective surfaces under the relaxed assumptions that (i) a compact array of only two microphones is available, (ii) emitter and receivers are not synchronized, and (iii) both the excitation signals and the impulse responses of the enclosures are unknown. |
G. Bologni; R. Heusdens; J. Martinez; |
| 94 | Detecting Acoustic Reflectors Using A Robot�s Ego-Noise Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a method to estimate the proximity of an acoustic reflector, e.g., a wall, using ego-noise, i.e., the noise produced by the moving parts of a listening robot. |
U. Saqib; A. Deleforge; J. R. Jensen; |
| 95 | Prediction of Object Geometry from Acoustic Scattering Using Convolutional Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The present work proposes a method to infer object geometry from scattering features by training convolutional neural networks. |
Z. Fan; V. Vineet; C. Lu; T. W. Wu; K. McMullen; |
| 96 | Blind Amplitude Estimation of Early Room Reflections Using Alternating Least Squares Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents a preliminary attempt to blindly estimate reflection amplitudes. |
T. Shlomo; B. Rafaely; |
| 97 | Acoustic Analysis and Dataset of Transitions Between Coupled Rooms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents the measurement and analysis of a dataset of spatial room impulse responses for the transition between four coupled room pairs. |
T. McKenzie; S. J. Schlecht; V. Pulkki; |
| 98 | On Loss Functions for Deep-Learning Based T60 Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a composite classification- and regression-based cost function for training a deep neural network that predicts T60 for a variety of reverberant signals. |
Y. Li; Y. Liu; D. S. Williamson; |
| 99 | Towards Listening to 10 People Simultaneously: An Efficient Permutation Invariant Training of Audio Source Separation Using Sinkhorn�s Algorithm Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this limitation, this paper proposes a SinkPIT, a novel variant of the PIT losses, which is much more efficient than the ordinary PIT loss when N is large. |
H. Tachibana; |
| 100 | Accelerating Auxiliary Function-Based Independent Vector Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we investigate techniques which accelerate the convergence of the AuxIVA update rules without extra computational cost. |
A. Brendel; W. Kellermann; |
| 101 | One-Shot Conditional Audio Filtering of Arbitrary Sounds Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider the problem of separating a particular sound source from a single-channel mixture, based on only a short sample of the target source (from the same recording). |
B. Gfeller; D. Roblek; M. Tagliasacchi; |
| 102 | Low Latency Online Blind Source Separation Based on Joint Optimization with Blind Dereverberation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a method to solve this problem by integrating BSS with Weighted Prediction Error (WPE) based dereverberation. |
T. Ueda; T. Nakatani; R. Ikeshita; K. Kinoshita; S. Araki; S. Makino; |
| 103 | Autoregressive Fast Multichannel Nonnegative Matrix Factorization For Joint Blind Source Separation And Dereverberation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper describes a joint blind source separation and dereverberation method that works adaptively and efficiently in a reverberant noisy environment. |
K. Sekiguchi; Y. Bando; A. A. Nugraha; M. Fontaine; K. Yoshii; |
| 104 | Phase Recovery with Bregman Divergences for Audio Source Separation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to reformulate phase recovery in audio source separation as a minimization problem involving Bregman divergences. |
P. Magron; P. -H. Vial; T. Oberlin; C. F�votte; |
| 105 | Adversarial Attacks on Audio Source Separation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we reformulate various adversarial attack methods for the audio source separation problem and intensively investigate them under different attack conditions and target models. |
N. Takahashi; S. Inoue; Y. Mitsufuji; |
| 106 | Maximum A Posteriori Estimator for Convolutive Sound Source Separation with Sub-Source Based NTF Model and The Localization Probabilistic Prior on The Mixing Matrix Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present a method for the separation of sound source signals recorded using multiple microphones in a reverberant room. |
M. Fras; K. Kowalczyk; |
| 107 | Unified Gradient Reweighting for Model Biasing with Applications to Source Separation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a simple, unified gradient reweighting scheme, with a lightweight modification to bias the learning process of a model and steer it towards a certain distribution of results. |
E. Tzinis; D. Bralios; P. Smaragdis; |
| 108 | Melon Playlist Dataset: A Public Dataset for Audio-Based Playlist Generation and Music Tagging Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present Melon Playlist Dataset, a public dataset of mel-spectrograms for 649,091 tracks and 148,826 associated playlists annotated by 30,652 different tags. |
A. Ferraro; et al. |
| 109 | Investigating The Efficacy of Music Version Retrieval Systems for Setlist Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an end-to-end workflow that identifies relevant metadata and timestamps of live music performances using a version identification system. |
F. Yesiler; E. Molina; J. Serr�; E. G�mez; |
| 110 | Instrument Classification of Solo Sheet Music Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we train AWD-LSTM, GPT-2, and RoBERTa models on solo sheet music images from IMSLP for eight different instruments. |
K. Ji; D. Yang; T. Tsai; |
| 111 | Bytecover: Cover Song Identification Via Multi-Loss Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present in this paper ByteCover, which is a new feature learning method for cover song identification (CSI). |
X. Du; Z. Yu; B. Zhu; X. Chen; Z. Ma; |
| 112 | Multi-Task Self-Supervised Pre-Training for Music Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we apply self-supervised and multi-task learning methods for pre-training music encoders, and explore various design choices including encoder architectures, weighting mechanisms to combine losses from multiple tasks, and worker selections of pretext tasks. |
H. -H. Wu; et al. |
| 113 | Towards Explaining Expressive Qualities in Piano Recordings: Transfer of Explanatory Features Via Acoustic Domain Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we show that by utilising unsupervised domain adaptation together with receptive-field regularised deep neural networks, it is possible to significantly improve generalisation to this domain. |
S. Chowdhury; G. Widmer; |
| 114 | Supervised Chorus Detection for Popular Music Using Convolutional Neural Network and Multi-Task Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a novel supervised approach to detecting the chorus segments in popular music. |
J. -C. Wang; J. B. L. Smith; J. Chen; X. Song; Y. Wang; |
| 115 | Structure-Aware Audio-to-Score Alignment Using Progressively Dilated Convolutional Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel method to detect such differences between the score and performance for a given piece of music using progressively dilated convolutional neural networks. |
R. Agrawal; D. Wolff; S. Dixon; |
| 116 | Language-Sensitive Music Emotion Recognition Models: Are We Really There Yet? Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents additional investigation on our approach, which reveals that: (1) performing pretraining with speech in a mixture of languages yields similar results than for specific languages – the pretraining phase appears not to exploit particular language features, (2) the music in Mandarin dataset consistently results in poor classification performance – we found low agreement in annotations, and (3) novel methodologies for representation learning (Contrastive Predictive Coding) may exploit features from both languages (i.e., pretraining on a mixture of languages) and improve classification of music emotions in both languages. |
J. S. G�mez-Ca��n; E. Cano; A. G. Pandrea; P. Herrera; E. G�mez; |
| 117 | Leveraging The Structure of Musical Preference in Content-Aware Music Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose instead to leverage a model of musical preference which originates from the field of music psychology. |
P. Magron; C. F�votte; |
| 118 | Low Resource Audio-To-Lyrics Alignment from Polyphonic Music Recordings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we present a novel method that performs audio-to-lyrics alignment with a low memory consumption footprint regardless of the duration of the music recording. |
E. Demirel; S. Ahlb�ck; S. Dixon; |
| 119 | Multimodal Metric Learning for Tag-Based Music Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate three ideas to successfully introduce multimodal metric learning for tag-based music retrieval: elaborate triplet sampling, acoustic and cultural music information, and domain-specific word embeddings. |
M. Won; S. Oramas; O. Nieto; F. Gouyon; X. Serra; |
| 120 | Learning Contextual Tag Embeddings for Cross-Modal Alignment of Audio and Tags Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we propose a method for learning audio representations using an audio autoencoder (AAE), a general word embed-dings model (WEM), and a multi-head self-attention (MHA) mechanism. |
X. Favory; K. Drossos; T. Virtanen; X. Serra; |
| 121 | Efficient End-to-End Audio Embeddings Generation for Audio Classification on Target Applications Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We describe a general-purpose end-to-end audio embeddings generator that can be easily adapted to various acoustic scene and event classification applications. |
P. Lopez-Meyer; J. A. del Hoyo Ontiveros; H. Lu; G. Stemmer; |
| 122 | Text-to-Audio Grounding: Building Correspondence Between Captions and Sound Events Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on such, we propose the text-to-audio grounding (TAG) task, which interactively considers the relationship be-tween audio processing and language understanding. |
X. Xu; H. Dinkel; M. Wu; K. Yu; |
| 123 | Multi-View Audio And Music Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose in this work a multi-view learning approach for audio and music classification. |
H. Phan; et al. |
| 124 | Audio-Visual Event Recognition Through The Lens of Adversary Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work aims to study several key questions related to multimodal learning through the lens of adversarial noises: 1) The trade-off between early/middle/late fusion affecting its robustness and accuracy 2) How does different frequency/time domain features contribute to the robustness? |
J. B. Li; K. Ma; S. Qu; P. -Y. Huang; F. Metze; |
| 125 | DCASENET: An Integrated Pretrained Deep Neural Network for Detecting and Classifying Acoustic Scenes and Events Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose three architectures of deep neural networks that are integrated to simultaneously perform acoustic scene classification, audio tagging, and sound event detection. |
J. -w. Jung; H. -j. Shim; J. -h. Kim; H. -J. Yu; |
| 126 | A Curated Dataset of Urban Scenes for Audio-Visual Scene Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces a curated dataset of urban scenes for audio-visual scene analysis which consists of carefully selected and recorded material. |
S. Wang; A. Mesaros; T. Heittola; T. Virtanen; |
| 127 | Improving Sound Event Detection Metrics: Insights from DCASE 2020 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper compares conventional event-based and segment-based criteria against the Polyphonic Sound Detection Score (PSDS)’s intersection-based criterion, over a selection of systems from DCASE 2020 Challenge Task 4. |
G. Ferroni; et al. |
| 128 | Artificially Synthesising Data for Audio Classification and Segmentation to Improve Speech and Music Detection in Radio Broadcast Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we present a novel procedure that artificially synthesises data that resembles radio signals. |
S. Venkatesh; et al. |
| 129 | LSSED: A Large-Scale Dataset and Benchmark for Speech Emotion Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present LSSED, a challenging large-scale english speech emotion dataset, which has data collected from 820 subjects to simulate real- world distribution. |
W. Fan; X. Xu; X. Xing; W. Chen; D. Huang; |
| 130 | Enhancing Audio Augmentation Methods with Consistency Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper investigates the use of training objectives that explicitly impose this consistency constraint, and how it can impact downstream audio classification tasks. |
T. Iqbal; K. Helwani; A. Krishnaswamy; W. Wang; |
| 131 | Fast Threshold Optimization for Multi-Label Audio Tagging Using Surrogate Gradient Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we consider having at disposal a trained classifier and we seek to automatically optimize the decision thresholds according to a performance metric of interest, in our case F-measure (micro-F1). |
T. Pellegrini; T. Masquelier; |
| 132 | Towards Efficient Models for Real-Time Deep Noise Suppression Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate reasonably small recurrent and convolutional-recurrent network architectures for speech enhancement, trained on a large dataset considering also reverberation. |
S. Braun; H. Gamper; C. K. A. Reddy; I. Tashev; |
| 133 | Teacher-Student Learning for Low-Latency Online Speech Enhancement Using Wave-U-Net Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a low-latency online extension of wave-U-net for single-channel speech enhancement, which utilizes teacher-student learning to reduce the system latency while keeping the enhancement performance high. |
S. Nakaoka; L. Li; S. Inoue; S. Makino; |
| 134 | Learning Disentangled Feature Representations for Speech Enhancement Via Adversarial Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address such mismatch, we propose to learn noise-agnostic feature representations by disentanglement learning, which removes the unspecified noise factor, while keeping the specified factors of variation associated with the clean speech. |
N. Hou; C. Xu; E. S. Chng; H. Li; |
| 135 | Speech Enhancement Autoencoder with Hierarchical Latent Structure Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: A new hierarchical convolutional neural network-based autoencoder architecture called SEHAE (Speech Enhancement Hierarchical AutoEncoder) is introduced, in which the latent representation is decomposed into several parts that correspond to different scales. |
K. Oostermeijer; J. Du; Q. Wang; C. -H. Lee; |
| 136 | Variational Autoencoder for Speech Enhancement with A Noise-Aware Encoder Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To increase the robustness of the VAE, we propose to include noise information in the training phase by using a noise-aware encoder trained on noisy-clean speech pairs. |
H. Fang; G. Carbajal; S. Wermter; T. Gerkmann; |
| 137 | Guided Variational Autoencoder for Speech Enhancement with A Supervised Classifier Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to guide the variational autoencoder with a supervised classifier separately trained on noisy speech. |
G. Carbajal; J. Richter; T. Gerkmann; |
| 138 | An Extension of Sparse Audio Declipper to Multiple Measurement Vectors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes formulating declipping as a constrained multiple measurement vector (MMV) optimization problem that has a ${\ell _{2,0}}$ group norm as its cost function for further improving the state-of-the-art declipping method SParse Audio DEclipper (SPADE). |
S. Emura; N. Harada; |
| 139 | Real-Time Speech Frequency Bandwidth Extension Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we propose a lightweight model for frequency bandwidth extension of speech signals, increasing the sampling frequency from 8kHz to 16kHz while restoring the high frequency content to a level almost indistinguishable from the 16kHz ground truth. |
Y. Li; M. Tagliasacchi; O. Rybakov; V. Ungureanu; D. Roblek; |
| 140 | Bandwidth Extension Is All You Need Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a new bandwidth extension (BWE) method that expands 8-16kHz speech signals to 48kHz. |
J. Su; Y. Wang; A. Finkelstein; Z. Jin; |
| 141 | Audio Dequantization Using (Co)Sparse (Non)Convex Methods Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: It reviews the state-of-the-art sparsity-based approaches and proposes several new methods. |
P. Z�vi�ka; P. Rajmic; O. Mokr�; |
| 142 | Source-Aware Neural Speech Coding for Noisy Speech Compression Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces a novel neural network-based speech coding system that can process noisy speech effectively. |
H. Yang; K. Zhen; S. Beack; M. Kim; |
| 143 | Enhancing Into The Codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on VQ-VAE autoencoders with WaveRNN decoders, we develop compressor-enhancer encoders and accompanying decoders, and show that they operate well in noisy conditions. |
J. Casebeer; V. Vale; U. Isik; J. -M. Valin; R. Giri; A. Krishnaswamy; |
| 144 | Speech Enhancement with Mixture of Deep Experts with Clean Clustering Pre-Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study we present a mixture of deep experts (MoDE) neural-network architecture for single microphone speech enhancement. |
S. E. Chazan; J. Goldberger; S. Gannot; |
| 145 | A Novel NMF-HMM Speech Enhancement Algorithm Based on Poisson Mixture Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel non-negative matrix factorization (NMF) and hidden Markov model (NMF-HMM) based speech enhancement algorithm, which employs a Poisson mixture model (PMM). |
Y. Xiang; L. Shi; J. L. H�jvang; M. H�jfeldt Rasmussen; M. G. Christensen; |
| 146 | Phoneme-Based Distribution Regularization for Speech Enhancement Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim to bridge this gap by extracting phoneme identities to help speech enhancement. |
Y. Liu; X. Peng; Z. Xiong; Y. Lu; |
| 147 | Compressed Representation of Cepstral Coefficients Via Recurrent Neural Networks for Informed Speech Enhancement Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We investigate a hybrid strategy made of signal processing and RNN (Recurrent Neural Networks) to calculate and compress cepstral coefficients: these are descriptors of the speech signal, which can be embedded in the signal itself and used at the receiver?s end to perform an Informed Speech Enhancement. |
C. Chermaz; D. Leuchtmann; S. Tanner; R. Wattenhofer; |
| 148 | Optimizing Short-Time Fourier Transform Parameters Via Gradient Descent Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we show an approach that allows us to obtain a gradient for STFT parameters with respect to arbitrary cost functions, and thus enable the ability to employ gradient descent optimization of quantities like the STFT window length, or the STFT hop size. |
A. Zhao; K. Subramani; P. Smaragdis; |
| 149 | Iterative Geometry Calibration from Distance Estimates for Wireless Acoustic Sensor Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present an approach to geometry calibration in wireless acoustic sensor networks, whose nodes are assumed to be equipped with a compact microphone array. |
T. Gburrek; J. Schmalenstroeer; R. Haeb-Umbach; |
| 150 | On The Design of Square Differential Microphone Arrays with A Multistage Structure Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: It presents a multistage approach, which first divides an SDMA composed of M2 microphones into (M – 1)2 subarrays with each subarray being a 2 ? 2 square array formed by four adjacent microphones. |
X. Zhao; G. Huang; J. Benesty; J. Chen; I. Cohen; |
| 151 | Arrays of First-Order Steerable Differential Microphones Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider arbitrarily shaped planar arrays of DMA units. |
F. Borra; A. Bernardini; I. Bertuletti; F. Antonacci; A. Sarti; |
| 152 | Planar Array Geometry Optimization for Region Sound Acquisition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper studies the problem of geometry optimization for planar arrays and it develops a genetic optimization algorithm that can optimize the positions of the sensors, thereby maximizing the directivity factor (DF) with a constrained level of white noise gain (WNG) given the number of microphones, the region in which they should be placed, and the interested range of steering. |
X. Chen; C. Pan; J. Chen; J. Benesty; |
| 153 | Estimation of Microphone Clusters in Acoustic Sensor Networks Using Unsupervised Federated Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we present a privacy-aware method for estimating source-dominated microphone clusters in the context of acoustic sensor networks (ASNs). |
A. Nelus; R. Glitza; R. Martin; |
| 154 | Misalignment Recognition in Acoustic Sensor Networks Using A Semi-Supervised Source Estimation Method and Markov Random Fields Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider the problem of acoustic source localization by acoustic sensor networks (ASNs) using a promising, learning-based technique that adapts to the acoustic environment. |
G. F. Miller; A. Brendel; W. Kellermann; S. Gannot; |
| 155 | Rotation-Robust Beamforming Based on Sound Field Interpolation with Regularly Circular Microphone Array Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel framework of beamforming robust for a microphone array rotation. |
Y. Wakabayashi; K. Yamaoka; N. Ono; |
| 156 | Sparse Recovery Beamforming and Upscaling in The Ray Space Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore a method to upscale an array beyond the limits imposed by the inter-microphone distances associated with the array and the concomitant spatial aliasing. |
S. Yu; C. Jin; F. Antonacci; A. Sarti; |
| 157 | Combined Differential Beamforming With Uniform Linear Microphone Arrays Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: It presents a method for the design of differential beamformers with uniform linear arrays. |
G. Huang; Y. Wang; J. Benesty; I. Cohen; J. Chen; |
| 158 | Polynomial Matrix Eigenvalue Decomposition of Spherical Harmonics for Speech Enhancement Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a PEVD algorithm that uses only the lower dimension eigenbeams for speech enhancement at a significantly lower computation cost. |
V. W. Neo; C. Evers; P. A. Naylor; |
| 159 | A Parametric Unconstrained Binaural Beamformer Based Noise Reduction and Spatial Cue Preservation for Hearing-Assistive Devices Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a parametric unconstrained binaural (PUB) beamformer, which can achieve a trade-off between noise reduction and binaural cue preservation. |
J. Zhang; |
| 160 | A Simplified Wiener Beamformer Based on Covariance Matrix Modelling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To deal with this problem, we propose a general method by parametric modeling the covariance matrices of speech and noise, which leads to a simplified Wiener beamformer. |
F. Zhang; C. Pan; J. Benesty; J. Chen; |
| 161 | Control Architecture of The Double-Cross-Correlation Processor for Sampling-Rate-Offset Estimation in Acoustic Sensor Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper converts the mechanism of offline multi-stage processing into a continuous feedback-control loop comprising a controlled ASRC unit followed by an online implementation of DXCP-based SRO estimation. |
A. Chinaev; S. Wienand; G. Enzner; |
| 162 | Deficient Basis Estimation of Noise Spatial Covariance Matrix for Rank-Constrained Spatial Covariance Matrix Estimation Method in Blind Speech Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed a new algorithmic extension of RCSCME. |
Y. Kondo; Y. Kubo; N. Takamune; D. Kitamura; H. Saruwatari; |
| 163 | Reducing Modal Error Propagation Through Correcting Mismatched Microphone Gains Using Rapid Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: A method for reducing the error propagation in modes by correcting the mismatched microphone gains is proposed, where RAndom PerturbatIons for Diffuse-field (RAPID) is used to design filters for correcting the mismatch. |
N. Akbar; G. Dickins; M. R. P. Thomas; P. Samarasinghe; T. Abhayapala; |
| 164 | Evaluation and Comparison of Three Source Direction-of-Arrival Estimators Using Relative Harmonic Coefficients Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a compact evaluation and comparison between two existing RHC based DOA estimators: (i) a method using a full grid search over the two-dimensional (2-D) directional space, (ii) a decoupled estimator which uses one-dimensional (1-D) search to separately localize the source’s elevation and azimuth. |
Y. Hu; P. N. Samarasinghe; S. Gannot; T. D. Abhayapala; |
| 165 | Network-Aware Optimal Microphone Channel Selection in Wireless Acoustic Sensor Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the vital problem of selecting the most useful microphones in wireless acoustic sensor networks, this paper proposes a novel, general-purpose approach that accounts for both acoustic and network aspects and remains application-agnostic for broad applicability. |
M. Gunther; H. Afifi; A. Brendel; H. Karl; W. Kellermann; |
| 166 | Supervised Direct-Path Relative Transfer Function Learning for Binaural Sound Source Localization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a supervised DP-RTF learning method with deep neural networks for robust binaural sound source localization. |
B. Yang; X. Li; H. Liu; |
| 167 | Cross-Modal Spectrum Transformation Network for Acoustic Scene Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we introduce an acoustic spectrum transformation network where traditional log-mel spectrums are transformed into imagined visual features (IVF). |
Y. Liu; A. Neophytou; S. Sengupta; E. Sommerlade; |
| 168 | Domestic Activities Clustering From Audio Recordings Using Convolutional Capsule Autoencoder Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we propose a method for domestic activities clustering using a convolutional capsule autoencoder network (CCAN). |
Z. Lin; et al. |
| 169 | Sound Event Detection and Separation: A Benchmark on Desed Synthetic Soundscapes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a benchmark of state-of-the-art sound event detection systems (SED). |
N. Turpault; et al. |
| 170 | A Two-Stage Approach to Device-Robust Acoustic Scene Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed. |
H. Hu; et al. |
| 171 | Subspectral Normalization for Neural Audio Data Processing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce SubSpectral Normalization (SSN), which splits the input frequency dimension into several groups (sub-bands) and performs a different normalization for each group. |
S. Chang; H. Park; J. Cho; H. Park; S. Yun; K. Hwang; |
| 172 | Slow-Fast Auditory Streams for Audio Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a two-stream convolutional network for audio recognition, that operates on time-frequency spectrogram inputs. |
E. Kazakos; A. Nagrani; A. Zisserman; D. Damen; |
| 173 | Impact of Sound Duration and Inactive Frames on Sound Event Detection Performance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the impact of sound duration and inactive frames on SED performance by introducing four loss functions, such as simple reweighting loss, inverse frequency loss, asymmetric focal loss, and focal batch Tversky loss. |
K. Imoto; S. Mishima; Y. Arai; R. Kondo; |
| 174 | A New DCASE 2017 Rare Sound Event Detection Benchmark Under Equal Training Data: CRNN With Multi-Width Kernels Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a new CRNN model for rare SED. |
J. Baumann; P. Meyer; T. Lohrenz; A. Roy; M. Papendieck; T. Fingscheidt; |
| 175 | Room Adaptive Conditioning Method for Sound Event Classification in Reverberant Environments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate this problem, we propose a conditioning method that provides room impulse response (RIR) information to help the network become less sensitive to environmental information and focus on classifying the desired sound. |
J. Lee; D. Lee; H. -S. Choi; K. Lee; |
| 176 | Sound Event Detection Based on Curriculum Learning Considering Learning Difficulty of Events Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To utilize the curriculum learning, we propose a new objective function for SED, wherein the events are trained from easy-to difficult-to-train events. |
N. Tonami; K. Imoto; Y. Okamoto; T. Fukumori; Y. Yamashita; |
| 177 | Sound Event Detection in Urban Audio with Single and Multi-Rate Pcen Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this article, we experiment using PCEN spectrograms as an alternative method for SED in urban audio using the UrbanSED dataset, demonstrating per-class improvements based on parameter configuration. |
C. Ick; B. McFee; |
| 178 | An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Two open problems are addressed in this paper. Firstly, to detect overlapping sound events of the same type but with different DoAs, we propose to use a trackwise output format and solve the accompanying track permutation problem with permutation-invariant training. Multi-head self-attention is further used to separate tracks. Secondly, a previous finding is that, by using hard parameter-sharing, SELD suffers from a performance loss compared with learning the subtasks separately. |
Y. Cao; T. Iqbal; Q. Kong; F. An; W. Wang; M. D. Plumbley; |
| 179 | Lightweight and Interpretable Neural Modeling of An Audio Distortion Effect Using Hyperconditioned Differentiable Biquads Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose using differentiable cascaded biquads to model an audio distortion effect. |
S. Nercessian; A. Sarroff; K. J. Werner; |
| 180 | Attacking and Defending Behind A Psychoacoustics-Based Captcha Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a novel audio CAPTCHA system that requires a user to respond immediately after hearing a short and easy-to-remember cue in its mixture with background music. |
C. -H. Huang; P. -H. Wu; Y. -W. Liu; S. -H. Wu; |
| 181 | Double-DCCCAE: Estimation of Body Gestures From Speech Waveform Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents an approach for body-motion estimation from audio-speech waveform, where context information in both input and output streams is taken in to account without using recurrent models. |
J. Lu; T. Liu; S. Xu; H. Shimodaira; |
| 182 | Investigating Local and Global Information for Automated Audio Captioning with Transfer Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper first proposes a topic model for audio descriptions, comprehensively analyzing the hierarchical audio topics that are commonly covered. We then explore a transfer learning scheme to access local and global information. |
X. Xu; H. Dinkel; M. Wu; Z. Xie; K. Yu; |
| 183 | Unidirectional Memory-Self-Attention Transducer for Online Speech Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Memory-Self-Attention (MSA), which adds history information into the Restricted-Self-Attention unit. |
J. Luo; J. Wang; N. Cheng; J. Xiao; |
| 184 | Accdoa: Activity-Coupled Cartesian Direction of Arrival Representation for Sound Event Localization And Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these problems, we propose an activity-coupled Cartesian DOA (ACCDOA) representation, which assigns a sound event activity to the length of a corresponding Cartesian DOA vector. |
K. Shimada; Y. Koyama; N. Takahashi; S. Takahashi; Y. Mitsufuji; |
| 185 | Seen and Unseen Emotional Style Transfer for Voice Conversion with A New Emotional Speech Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel framework based on variational auto-encoding Wasserstein generative adversarial network (VAW-GAN), which makes use of a pre-trained speech emotion recognition (SER) model to transfer emotional style during training and at run-time inference. |
K. Zhou; B. Sisman; R. Liu; H. Li; |
| 186 | U-Convolution Based Residual Echo Suppression with Multiple Encoders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an efficient end-to-end neural network that can estimate near-end speech using a U-convolution block by exploiting various signals to achieve residual echo suppression (RES). |
E. Kim; J. -J. Jeon; H. Seo; |
| 187 | A Multi-Channel Temporal Attention Convolutional Neural Network Model for Environmental Sound Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an effective convolutional neural network structure with a multichannel temporal attention (MCTA) block, which applies a temporal attention mechanism within each channel of the embedded features to extract channel-wise relevant temporal information. |
Y. Wang; C. Feng; D. V. Anderson; |
| 188 | A General Network Architecture for Sound Event Localization and Detection Using Transfer Learning and Recurrent Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a general network architecture for SELD in which the SELD network comprises sub-networks that are pre-trained to solve SED and DOA estimation independently, and a recurrent layer that combines the SED and DOA estimation outputs into SELD outputs. |
T. N. T. Nguyen; et al. |
| 189 | Robust Recursive Least M-Estimate Adaptive Filter for The Identification of Low-Rank Acoustic Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To identify acoustic systems (which are low-rank in nature) in non-Gaussian and Gaussian noise, a robust recursive least M-estimate adaptive filtering algorithm is developed in this paper by applying the nearest Kronecker product to decompose the acoustic impulse response. |
H. He; J. Chen; J. Benesty; Y. Yu; |
| 190 | Noise-Robust Adaptation Control for Supervised Acoustic System Identification Exploiting A Noise Dictionary Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a noise-robust adaptation control strategy for block-online supervised acoustic system identification by exploiting a noise dictionary. |
T. Haubner; A. Brendel; M. Elminshawi; W. Kellermann; |
| 191 | Interpolation of Irregularly Sampled Frequency Response Functions Using Convolutional Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we propose to use Convolutional Autoencoders (CA) for Frequency Response Function (FRF) interpolation from grids with different subsampling schemes. |
M. Acerbi; R. Malvermi; M. Pezzoli; F. Antonacci; A. Sarti; R. Corradi; |
| 192 | Effective Rank-Based Estimation of The Coherent-to-Diffuse Power Ratio Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: A CDR estimator whose design is based on this premise is devised in this contribution. |
H. W. L�llmann; A. Brendel; W. Kellermann; |
| 193 | Room Impulse Response Interpolation from A Sparse Set of Measurements Using A Modal Architecture Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel method for 2D interpolation of room modes from a sparse set of RIR measurements that are non-uniformly sampled within a space. |
O. Das; P. Calamia; S. V. Amengual Gari; |
| 194 | Processing Pipelines for Efficient, Physically-Accurate Simulation of Microphone Array Signals in Dynamic Sound Scenes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: A new approach, in which the filter kernels are obtained using principal component analysis from time-aligned impulse responses, is proposed. |
A. H. Moore; R. R. Vos; P. A. Naylor; M. Brookes; |
| 195 | A Classifier for Improving Cause and Effect in SSVEP-based BCIs for Individuals with Complex Communication Disorders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present CCACUSUM, a classifier for steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that determines whether a user is attending to a flickering stimulus or is at rest. |
H. Habibzadeh; O. Zhou; J. J. S. Norton; T. M. Vaughan; D. -S. Zois; |
| 196 | Saga: Sparse Adversarial Attack on EEG-Based Brain Computer Interface Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we conduct the first in-depth study on the robustness of EEG analytics under sparse perturbations and propose the first Sparse Adversarial eeG Attack, SAGA, to identify weakness of EEG analytics. |
B. Feng; Y. Wang; Y. Ding; |
| 197 | Riemannian Geometry on Connectivity for Clinical BCI Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To increase the accuracy of BCI systems, we propose an approach grounded on Riemannian geometry that extends this framework to functional connectivity measures. |
M. -C. Corsi; F. Yger; S. Chevallier; C. No�s; |
| 198 | Decoding Music Attention from �EEG Headphones�: A User-Friendly Auditory Brain-Computer Interface Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel BCI system using music stimuli that relies on brain signals collected via Smartfones, an EEG recording device integrated into a pair of headphones. |
W. W. An; et al. |
| 199 | Mitigating Inter-Subject Brain Signal Variability FOR EEG-Based Driver Fatigue State Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a subject- independent EEG-based driver fatigue state (i.e., awake, tired, and drowsy) classification model that mitigates a performance gap between subjects. |
S. Hwang; S. Park; D. Kim; J. Lee; H. Byun; |
| 200 | A Deep Spatio-Temporal Model for EEG-Based Imagined Speech Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Consequently, in this work, we propose an imagined speech Brain-Computer-Interface (BCI) using Electroencephalogram (EEG) signals. |
P. Kumar; E. Scheme; |
| 201 | Incorporating Uncertainty In Data Labeling Into Detection of Brain Interictal Epileptiform Discharges From EEG Using Weighted Optimization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we incorporate this probability in an IED detection system which combines spatial component analysis (SCA) with the IED probabilities referred to as SCA-IEDP-based method. |
B. Abdi-Sargezeh; A. Valentin; G. Alarcon; S. Sanei; |
| 202 | Multi-Level Reversible Encryption for ECG Signals Using Compressive Sensing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a compressive sensing based multi-level encryption to ECG signals to mask possible heartbeat anomalies from semi-authorized users, while preserving the beat structure for heart rate monitoring. |
M. Impi�; M. Yama�; J. Raitoharju; |
| 203 | Validating The Inspired Sinewave Technique to Measure Lung Heterogeneity Compared to Atelectasis & Over-Distended Volume in Computed Tomography Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Six anaesthetised pigs were studied after surfactant depletion by saline-lavage. |
M. C. Tran; et al. |
| 204 | A Patient-Invariant Model for Freezing of Gait Detection Aided By Wavelet Decomposition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a method for online detection of FoG using a wearable motion sensor. |
N. Ahmed; S. Singhal; V. Sharma; S. Bhattacharya; A. Sinha; A. Ghose; |
| 205 | Identification of Uterine Contractions By An Ensemble of Gaussian Processes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study contraction identification by processing noisy signals due to uterine activities. |
L. Yang; C. Heiselman; J. Gerald Quirk; P. M. Djuri; |
| 206 | Arrhythmia Classification with Heartbeat-Aware Transformer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed a novel neural network model which treats typical heartbeat classification task as ?Translation? problem. |
B. Wang; C. Liu; C. Hu; X. Liu; J. Cao; |
| 207 | Multi-Level Group Testing with Application to One-Shot Pooled COVID-19 Tests Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we study pooling-based COVID-19 tests. |
A. Cohen; N. Shlezinger; A. Solomon; Y. C. Eldar; M. M�dard; |
| 208 | Detection of Covid-19 Through The Analysis of Vocal Fold Oscillations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our goal is to validate this hypothesis, and to quantitatively characterize the changes observed to enable the detection of COVID-19 from voice. |
M. Al Ismail; S. Deshmukh; R. Singh; |
| 209 | Ct-Caps: Feature Extraction-Based Automated Framework for Covid-19 Disease Identification From Chest Ct Scans Using Capsule Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a Capsule network framework, referred to as the CT-CAPS, is presented to automatically extract distinctive features of chest CT scans. |
S. Heidarian; et al. |
| 210 | Few-Shot Learning for Ct Scan Based Covid-19 Diagnosis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to tackle the above issues, we propose a supervised domain adaption based COVID-19 CT diagnostic method which can perform effectively when only a small samples of labeled CT scans are available. |
Y. Jiang; H. Chen; H. Ko; D. K. Han; |
| 211 | Graph-Based Pyramid Global Context Reasoning With A Saliency- Aware Projection for Covid-19 Lung Infections Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle these issues, we propose a Graph-based Pyramid Global Context Reasoning (Graph-PGCR) module, which is capable of modeling long-range dependencies among disjoint infections as well as adapt size variation. |
H. Huang; et al. |
| 212 | Interpreting Glottal Flow Dynamics for Detecting Covid-19 From Voice Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a method that analyzes the differential dynamics of the glottal flow waveform (GFW) during voice production to identify features in them that are most significant for the detection of COVID-19 from voice. |
S. Deshmukh; M. Al Ismail; R. Singh; |
| 213 | Cycle Generative Adversarial Network Approaches to Produce Novel Portable Chest X-Rays Images for Covid-19 Diagnosis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, given the low availability of images of this recent disease, we present new approaches to artificially increase the dimensionality of portable chest X-ray datasets for COVID-19 diagnosis. |
D. I. Mor�s; J. de Moura; J. Novo; M. Ortega; |
| 214 | EEG-Based Emotion Classification Using Graph Signal Processing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we bring to bear graph signal processing (GSP) techniques to tackle the problem of automatic emotion recognition using brain signals. |
S. S. Saboksayr; G. Mateos; M. Cetin; |
| 215 | Granger Causality Based Directional Phase-Amplitude Coupling Measure Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a Granger causality (GC) based approach to estimate the direction of PAC. |
T. T. K. Munia; S. Aviyente; |
| 216 | REPAC: Reliable Estimation of Phase-Amplitude Coupling in Brain Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This contribution presents REPAC, a reliable and robust algorithm for modeling and detecting PAC events in EEG signals. |
G. Cisotto; |
| 217 | Subspace Oddity – Optimization on Product of Stiefel Manifolds for EEG Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel similarity-based classification method that relies on dimensionality reduction of EEG covariance matrices. |
M. Sayu Yamamoto; F. Yger; S. Chevallier; |
| 218 | Decentralized Motion Inference and Registration of Neuropixel Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a new registration method to partially correct for this motion. |
E. Varol; et al. |
| 219 | Dynamic Graph Learning Based on Graph Laplacian Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The purpose of this paper is to infer a global (collective) model of time-varying responses of a set of nodes as a dynamic graph, where the individual time series are respectively observed at each of the nodes. |
B. Jiang; Y. Yu; H. Krim; S. L. Smith; |
| 220 | Mutual Information Flows in A Bivariate Point Process Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we address that question using mutual information flows and establish a connection with Granger causality. |
S. Ahmed Pasha; V. Solo; |
| 221 | Uncertainty-Based Biological Age Estimation of Brain MRI Scans Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this initial study, we propose a new framework for organ-specific BA estimation utilizing 3D magnetic resonance image (MRI) scans. |
K. Armanious; S. Abdulatif; W. Shi; T. Hepp; S. Gatidis; B. Yang; |
| 222 | Sparse Representation of Complex-Valued FMRI Data Based on Hard Thresholding of Spatial Source Phase Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This study proposes a sparse representation method using SSP hard thresholding to achieve the sparsity of spatial components, enabling the use of initially complex-valued fMRI data and retaining the brain information embedded in noisy voxels and weak BOLD-related voxels with small phase values. |
J. -Y. Song; M. -Y. Qi; D. -P. Lv; C. -Y. Zhang; Q. -H. Lin; V. D. Calhoun; |
| 223 | Tucker Decomposition for Extracting Shared and Individual Spatial Maps from Multi-Subject Resting-State FMRI Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This study proposes to decompose multi-subject fMRI data in a natural three-way of voxel ? time ? subject via TKD. |
Y. Han; Q. -H. Lin; L. -D. Kuang; X. -F. Gong; F. Cong; V. D. Calhoun; |
| 224 | Riemannian Geometry-Based Decoding of The Directional Focus of Auditory Attention Using EEG Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we propose Riemannian geometry-based classification (RGC) as an alternative for this CSP approach, in which the covariance matrix of a new EEG segment is directly classified while taking its Riemannian structure into account. |
S. Geirnaert; T. Francart; A. Bertrand; |
| 225 | DFDM: A Deep Feature Decoupling Module for Lung Nodule Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel feature decoupling method to tackle two critical problems in the lung nodule segmentation task: (i) ambiguity of nodule boundary leads to the imprecise segmentation boundary and (ii) the high false positive rate of segmentation result. |
W. Chen; Q. Wang; S. Huang; X. Zhang; Y. Li; C. Liu; |
| 226 | Pyramid U-Net for Retinal Vessel Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose pyramid U-Net for accurate retinal vessel segmentation. |
J. Zhang; Y. Zhang; X. Xu; |
| 227 | A Probabilistic Model for Segmentation of Ambiguous 3D Lung Nodule Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end we propose a probabilistic generative segmentation model consisting of a V-Net and a conditional variational autoencoder. |
X. Long; et al. |
| 228 | Semi-Supervised Skin Lesion Segmentation with Learning Model Confidence Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, to solve this issue, we propose a novel confidence aware semi-supervised learning method based on a mean teacher scheme. |
Z. Xie; E. Tu; H. Zheng; Y. Gu; J. Yang; |
| 229 | A Hybrid Feature Enhancement Method for Gl And Segmentation In Histopathology Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a hybrid feature enhancement network (HFE-Net) for glandular segmentation is proposed, which includes a multi-scale local feature extraction block (MSLFEB) and a global feature enhancement block (GFEB). |
X. Wu; X. Li; K. Hu; Z. Chen; X. Gao; |
| 230 | Automated Multi-Organ Segmentation in Pet Images Using Cascaded Training of A 3d U-Net and Convolutional Autoencoder Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: As this transfer of information from CT/MRI to the PET domain is not always feasible, e.g. when the corresponding CT or MRI images are unavailable or corrupted by artifacts, we propose a novel approach to perform organ segmentation on the PET images directly. |
A. Liebgott; C. Lorenz; S. Gatidis; V. C. Vu; K. Nikolaou; B. Yang; |
| 231 | Improved Supervised Training of Physics-Guided Deep Learning Image Reconstruction with Multi-Masking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we propose to improve the performance and robustness of supervised training by utilizing randomness by retrospectively selecting only a subset of all the available measurements for data consistency units. |
B. Yaman; S. A. H. Hosseini; S. Moeller; M. Ak�akaya; |
| 232 | Fine-Grained Mri Reconstruction Using Attentive Selection Generative Adversarial Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the state-of-the-art methods in image generation, we propose a novel attention-based deep learning framework to provide high-quality MRI reconstruction. |
J. Liu; M. Yaghoobi; |
| 233 | Ensure: Ensemble Stein�s Unbiased Risk Estimator for Unsupervised Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an ENsemble SURE (ENSURE) approach to train a deep network only from undersampled measurements. |
H. K. Aggarwal; A. Pramanik; M. Jacob; |
| 234 | Ultrasound Elasticity Imaging Using Physics-Based Models and Learning-Based Plug-and-Play Priors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Integrating learning-based priors with physical forward models for ultrasound elasticity imaging, we present a joint reconstruction framework which guarantees that learning driven reconstructions are consistent with the underlying physics. |
N. Mohammadi; M. M. Doyley; M. Cetin; |
| 235 | A Periodic Frame Learning Approach for Accurate Landmark Localization in M-Mode Echocardiography Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel two-stage frame-level detection and heatmap regression model for accurate landmark localization in m-mode echocardiography, which promotes better integration between global context information and local appearance. |
Y. Tian; S. Xu; L. Guo; F. Cong; |
| 236 | A Bias-Reducing Loss Function for CT Image Denoising Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel approach to designing a loss function that penalizes variance and bias differently. |
M. Nagare; R. Melnyk; O. Rahman; K. D. Sauer; C. A. Bouman; |
| 237 | Learning Binary Semantic Embedding for Breast Histology Image Classification and Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issues, we propose a novel method for Learning Binary Semantic Embedding (LBSE). |
X. Kang; X. Liu; X. Nie; Y. Yin; |
| 238 | Channel Attention Residual U-Net for Retinal Vessel Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a new deep learning model, namely Channel Attention Residual U-Net (CAR-UNet), to accurately segment retinal vascular and non-vascular pixels. |
C. Guo; M. Szemenyei; Y. Hu; W. Wang; W. Zhou; Y. Yi; |
| 239 | CMIM: Cross-Modal Information Maximization For Medical Imaging Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an innovative framework that makes the most of available data by learning good representations of a multi-modal input that are resilient to modality dropping at test-time, using recent advances in mutual information maximization. |
T. Sylvain; et al. |
| 240 | Structure-Enhanced Attentive Learning For Spine Segmentation From Ultrasound Volume Projection Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel framework to improve the segmentation accuracy on spine images via structure-enhanced attentive learning. |
R. Zhao; et al. |
| 241 | Foveal Avascular Zone Segmentation of Octa Images Using Deep Learning Approach with Unsupervised Vessel Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To simultaneously implement vessel and accurate FAZ segmentation, an end-to-end trained network is proposed to achieve unsupervised vessel segmentation and supervised FAZ segmentation. |
Z. Liang; J. Zhang; C. An; |
| 242 | Acute Lymphoblastic Leukemia Detection Based on Adaptive Unsharpening and Deep Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, in this paper we propose the first machine learning-based approach able to enhance blood sample images by an adaptive unsharpening method. |
A. Genovese; M. S. Hosseini; V. Piuri; K. N. Plataniotis; F. Scotti; |
| 243 | Meta Ordinal Weighting Net For Improving Lung Nodule Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Meta Ordinal Weighting Network (MOW-Net) to explicitly align each training sample with a meta ordinal set (MOS) containing a few samples from all classes. |
Y. Lei; H. Shan; J. Zhang; |
| 244 | Deepnodule: Multi-Task Learning of Segmentation Bootstrap for Pulmonary Nodule Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome those barriers, we present a novel multi-task 3D convolutional network (DeepNodule) for simultaneous nodule detection and segmentation in a shared-and-fined manner. |
J. Li; K. Wang; D. Yang; X. Zhang; C. Liu; |
| 245 | Dense Attention Module for Accurate Pulmonary Nodule Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel pulmonary nodule detection framework and a novel 3D dense attention module (DAM) which can efficiently exploit the abundant 3D spatial features. |
J. Liu; J. Li; F. Xue; C. Wu; |
| 246 | Unsupervised Multimodal Image Registration with Adaptative Gradient Guidance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel multimodal registration framework, which leverages the deformation fields estimated from both: (i) the original to-be-registered image pair, (ii) their corresponding gradient intensity maps, and adaptively fuses them with the proposed gated fusion module. |
Z. Xu; J. Yan; J. Luo; X. Li; J. Jagadeesan; |
| 247 | Improving Intraoperative Liver Registration in Image-Guided Surgery with Learning-Based Reconstruction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome the problems caused by noisy, partial, and sparse intraoperative sampling, we propose a novel occupancy-learning-based mesh to point cloud registration and apply it to align the preoperative liver image to intraoperative samples. |
M. Jia; M. Kyan; |
| 248 | A New Framework Based on Transfer Learning for Cross-Database Pneumonia Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed a new framework based on transfer learning for cross-database pneumonia detection. |
X. Shan; Y. Wen; |
| 249 | Hierarchical Attention-Based Temporal Convolutional Networks for Eeg-Based Emotion Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to tackle these problems, a hierarchical attention-based temporal convolutional networks (HATCN) for efficient EEG-based emotion recognition is proposed. |
C. Li; B. Chen; Z. Zhao; N. Cummins; B. W. Schuller; |
| 250 | Deep Multiway Canonical Correlation Analysis For Multi-Subject Eeg Normalization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a deep learning framework to improve the correlation of electroencephalography (EEG) data recorded from multiple subjects engaged in an audio listening task. |
J. R. Katthi; S. Ganapathy; |
| 251 | Dynamic Graph Modeling Of Simultaneous EEG And Eye-Tracking Data For Reading Task Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a new approach, that we call AdaGTCN, for identifying human reader intent from Electroencephalogram (EEG) and Eye movement (EM) data in order to help differentiate between normal reading and task-oriented reading. |
P. Mathur; T. Mittal; D. Manocha; |
| 252 | Learning From Heterogeneous Eeg Signals with Differentiable Channel Reordering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose CHARM, a method for training a single neural network across inconsistent input channels. |
A. Saeed; D. Grangier; O. Pietquin; N. Zeghidour; |
| 253 | Enhancing Multi-Channel Eeg Classification with Gramian Temporal Generative Adversarial Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel method to synthesize multi-channel EEG in the form of Gramian Angular Field (GAF) images with a Gramian Temporal Generative Adversarial Network (GT-GAN). |
C. N. Enoch Kan; R. J. Povinelli; D. H. Ye; |
| 254 | A Novel Convolutional Neural Network Model to Remove Muscle Artifacts from EEG Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we introduce a novel convolutional neural network (CNN) with gradually ascending feature dimensions and downsampling in time series for removing muscle artifacts in EEG data. |
H. Zhang; C. Wei; M. Zhao; Q. Liu; H. Wu; |
| 255 | Multilabel 12-Lead Electrocardiogram Classification Using Beat to Sequence Autoencoders Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper investigates the multi-label, multi-class classification of ECG records into one or more of 27 possible medical diagnoses. |
A. W. Wong; A. Salimi; A. Hindle; S. V. Kalmady; P. Kaul; |
| 256 | Contrastive Embeddind Learning Method for Respiratory Sound Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the problems, we propose a contrastive embedding learning method, where the input is a contrastive tuple. |
W. Song; J. Han; H. Song; |
| 257 | Decoding Neural Representations of Rhythmic Sounds From Magnetoencephalography Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate how to extract rhythmic information embedded in the brain responses and to decode the original audio waveforms from the extracted information. |
P. -C. Chang; et al. |
| 258 | Low-Dimensional Denoising Embedding Transformer for ECG Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new method for ECG classification, called low-dimensional denoising embedding transformer (LDTF), which contains two components, i.e., low-dimensional denoising embedding (LDE) and transformer learning. |
J. Guan; W. Wang; P. Feng; X. Wang; W. Wang; |
| 259 | Self-Supervised Learning for Sleep Stage Classification with Predictive and Discriminative Contrastive Coding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The purpose of this paper is to learn efficient representations from raw electroencephalogram (EEG) signals for sleep stage classification via self-supervised learning (SSL). |
Q. Xiao; et al. |
| 260 | Length No Longer Matters: A Real Length Adaptive Arrhythmia Classification Model with Multi-Scale Convolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these problems, we propose a length adaptive arrhythmia classification model that can take advantage of raw ECG records of variable length. |
C. Han; F. Yu; P. Wang; R. Huang; X. Huang; L. Cui; |
| 261 | Few-Shot Learning for Decoding Surface Electromyography for Hand Gesture Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, in this work, we develop a novel hand gesture recognition framework based on the formulation of FewShot Learning (FSL) to infer the required output given only one or a few numbers of training examples. |
E. Rahimian; S. Zabihi; A. Asif; S. F. Atashzar; A. Mohammadi; |
| 262 | Deeplung Auscultation Using Acoustic Biomarkers for Abnormal Respiratory Sound Event Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to use two sets of diversified acoustic biomarkers extracted using Discrete Wavelet Transform (DWT) and deep encoded features from the intermediate layer of a pre-trained Audio Event Detection (AED) model trained using sounds from daily activities. |
U. Tiwari; S. Bhosale; R. Chakraborty; S. K. Kopparapu; |
| 263 | Speaker-Independent Brain Enhanced Speech Denoising Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel deep learning method referred to as the Brain Enhanced Speech Denoiser (BESD), that takes advantage of the attended auditory information present in the brain activity of the listener to denoise a multi-talker speech. |
M. Hosseini; L. Celotti; �. Plourde; |
| 264 | Shapelet Based Visual Assessment of Cluster Tendency in Analyzing Complex Upper Limb Motion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an unsupervised method for shapelet extraction using maximin shape sampling and shape-based distance computation for selecting key shapelets representing characteristic motion patterns. |
S. Datta; C. Karmakar; P. Rathore; M. Palaniswami; |
| 265 | Human-Centered Favorite Music Classification Using EEG-Based Individual Music Preference Via Deep Time-Series CCA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: A method to classify a user?s like or dislike musical pieces based on the extraction of his or her music preference is proposed in this paper. |
R. Sawata; T. Ogawa; M. Haseyama; |
| 266 | Multi-Scale and Multi-Region Facial Discriminative Representation for Automatic Depression Level Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For these reasons, we propose a multi-scale and multi-region fa-cial dynamic representation method to improve the prediction performance. |
M. Niu; J. Tao; B. Liu; |
| 267 | ECG Heart-Beat Classification Using Multimodal Image Fusion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel Image Fusion Model (IFM) for ECG heart-beat classification to overcome the weaknesses of existing machine learning techniques that rely either on manual feature extraction or direct utilization of 1D raw ECG signal. |
Z. Ahmad; A. Tabassum; L. Guan; N. Khan; |
| 268 | Estimation of Visual Features of Viewed Image From Individual and Shared Brain Information Based on FMRI Data Using Probabilistic Generative Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a method for estimation of visual features based on brain responses measured when subjects view images. |
T. Higashi; K. Maeda; T. Ogawa; M. Haseyama; |
| 269 | Hierarchical Pose Classification for Infant Action Analysis and Mental Development Assessment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a hierarchical pose classifier, given a baby image frame that com-bines the benefits of 3D human pose estimation and scene context information. |
J. Zhou; Z. Jiang; J. -H. Yoo; J. -N. Hwang; |
| 270 | On The Relationship Between Speech-Based Breathing Signal Prediction Evaluation Measures and Breathing Parameters Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper investigates whether there is a systematic relationship between the different objective measures used for training and evaluating the neural network models and the end-goal, i.e. estimation of breathing parameters such as, breathing rate and tidal volume. |
Z. Mostaani; V. Srikanth Nallanthighal; A. H�rm�; H. Strik; M. Magimai-Doss; |
| 271 | Prediction of Egfr Mutation Status in Lung Adenocarcinoma Using Multi-Source Feature Representations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we propose a hybrid framework, namely HC-DLR, to noninvasively predict EGFR mutation status by fusing multi-source features including low-level handcrafted radiomics (HCR) features, high-level deep learning-based radiomics (DLR) features, and demographics features. |
J. Cheng; J. Liu; M. Jiang; H. Yue; L. Wu; J. Wang; |
| 272 | Training Neural Networks with Domain Pattern-Aware Auxiliary Task for Sleep Staging Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Accordingly, we present an auxiliary classification task for sleep staging to enable NNs to exploit clinically significant EEG patterns in data. |
T. Lee; J. Hwang; H. Lee; |
| 273 | Classification of Expert-Novice Level Using Eye Tracking And Motion Data Via Conditional Multimodal Variational Autoencoder Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a semi-supervised anomaly detection approach that requires only sensor data of experts for training and identifies those of novices as anomalies. |
Y. Akamatsu; K. Maeda; T. Ogawa; M. Haseyama; |
| 274 | Gate Trimming: One-Shot Channel Pruning for Efficient Convolutional Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a one-shot global pruning approach called Gate Trimming (GT), which is more efficient to compress the CNNs. |
F. Yu; C. Han; P. Wang; X. Huang; L. Cui; |
| 275 | Deep S3PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we demonstrate that by restricting the solutions to lie in the range of a deep generative model, we can constrain the search space sufficiently to solve S3PR.Code associated with this work is available at https://github.com/computational-imaging/DeepS3PR. |
C. A. Metzler; G. Wetzstein; |
| 276 | Adversarial Attacks on Object Detectors with Limited Perturbations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel attack framework named DTTACK to fool both one-stage and two-stage object detectors with limited perturbations. |
Z. Shi; et al. |
| 277 | A Consensus Equilibrium Solution For Deep Image Prior Powered By Red Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we formulate DeepRED as a consensus equilibrium problem and set up a fixed-point algorithm for solving the equilibrium equations. |
R. Hyder; H. Mansour; Y. Ma; P. T. Boufounos; P. Wang; |
| 278 | Suremap: Predicting Uncertainty in Cnn-Based Image Reconstructions Using Stein�s Unbiased Risk Estimate Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we use Stein?s unbiased risk estimate (SURE) to develop per-pixel confidence intervals, in the form of heatmaps, for compressive sensing reconstruction using the approximate message passing (AMP) framework with CNN-based denoisers. |
R. Kitichotkul; C. A. Metzler; F. Ong; G. Wetzstein; |
| 279 | Multi-Initialization Meta-Learning with Domain Adaptation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To improve the performance on multi-modal tasks, we propose multi-initialization meta-learning with domain adaptation (MIML-DA) to tackle such domain shift. |
Z. Chen; D. Wang; |
| 280 | Stochastic Deep Unfolding for Imaging Inverse Problems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose SCRED-Net as a novel methodology that introduces a stochastic approximation to the unfolded regularization by denoising (RED) algorithm. |
J. Liu; Y. Sun; W. Gan; X. Xu; B. Wohlberg; U. S. Kamilov; |
| 281 | Fusion-Based Digital Image Correlation Framework for Strain Measurement Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this issue, we propose an end-to-end DIC framework incorporating the image fusion principle to achieve full-field strain measurement over the curved surface. |
L. Shi; D. Liu; M. Umeda; N. Hana; |
| 282 | Learning Sparsifying Transforms for Image Reconstruction in Electrical Impedance Tomography Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a blind compressed sensing algorithm, dubbed TL-EIT, which simultaneously optimizes the sparsifying transform and updates the reconstructed image. |
K. Yang; N. Borijindargoon; B. P. Ng; S. Ravishankar; B. Wen; |
| 283 | D-VDAMP: Denoising-Based Approximate Message Passing for Compressive MRI Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a CNN architecture for removing colored Gaussian noise and combine it with the recently proposed VDAMP algorithm, whose effective noise follows a predictable colored Gaussian distribution. |
C. A. Metzler; G. Wetzstein; |
| 284 | Empirically Accelerating Scaled Gradient Projection Using Deep Neural Network for Inverse Problems in Image Processing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we present a novel DNN-based convergent iterative algorithm that accelerates conventional optimization algorithms. |
B. H. Lee; S. Y. Chun; |
| 285 | Synthetic Aperture Acoustic Imaging with Deep Generative Model Based Source Distribution Prior Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to image large acoustic sources with a combination of synthetic aperture and their geometric structures modeled by a conditional generative adversarial network (cGAN). |
B. Fan; S. Das; |
| 286 | Non-Local Single Image DE-Raining Without Decomposition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: On top of a new insight in single image de-raining, a nonlocal de-raining algorithm is proposed in this paper to remove the rain streaks from the rainy image. |
C. Zheng; Z. Li; Y. Li; S. Wu; |
| 287 | Frame-Rate-Aware Aggregation for Efficient Video Super-Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In contrast to the previous works that perform explicit motion estimation and compensation, we propose a novel deep neural network which performs implicit motion estimation with frame-rate-based temporal aggregation. |
T. Isobe; F. Zhu; S. Wang; |
| 288 | Measurement Coding Framework with Adjacent Pixels Based Measurement Matrix for Compressively Sensed Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To further compress measurements, the output of block-based compressed sensing, this work presents a measurement coding framework using measurement-domain intra prediction. |
R. Wan; J. Zhou; B. Huang; H. Zeng; Y. Fan; |
| 289 | Multiview Sensing with Unknown Permutations: An Optimal Transport Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we take a fresh look at this problem through the lens of optimal transport (OT). |
Y. Ma; P. T. Boufounos; H. Mansour; S. Aeron; |
| 290 | A High-Frame-Rate Eye-Tracking Framework for Mobile Devices Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we tackle the tracking efficiency challenge and introduce GazeHFR, a biologic-inspired eye-tracking model specialized for mobile devices, offering both high accuracy and efficiency. |
Y. Chang; C. He; Y. Zhao; T. Lu; N. Gu; |
| 291 | Catiloc: Camera Image Transformer for Indoor Localization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper the problem of single image indoor camera localization has been addressed. |
A. Ghofrani; R. M. Toroghi; S. Mojtaba Tabatabaie; |
| 292 | Sar Image Autofocusing Using Wirtinger Calculus and Cauchy Regularization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, an optimization model using Cauchy regularization is proposed for simultaneous SAR image reconstruction and autofocusing. |
Z. -Y. Zhang; O. Pappas; A. Achim; |
| 293 | A Homogeneity-Based Multiscale Hyperspectral Image Representation for Sparse Spectral Unmixing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a computationally efficient multiscale representation method for hyperspectral data adapted to the unmixing problem. |
L. C. Ayres; S. J. M. de Almeida; J. C. M. Bermudez; R. A. Borsoi; |
| 294 | Learning to Estimate Kernel Scale and Orientation of Defocus Blur with Asymmetric Coded Aperture Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, we propose a deep-learning-based framework estimating the kernel scale and orientation of the defocus blur to ad-just lens focus rapidly. |
J. Li; Q. Dai; J. Wen; |
| 295 | Transmittance Regularizer for Binary Coded Aperture Design in A Computational Imaging End-to-end Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, this work proposes two transmittance regularizers that jointly induce binary en-tries and adjust the transmittance level to be incorporated in an E2E approach. |
J. Bacca; T. Gelvez; H. Arguello; |
| 296 | Fourier Transformation Autoencoders for Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces Fourier Trans-forms into AutoEncoders to demonstrate how the inclusion of a frequency domain presents less noisy features for a deep learning network to detect anomalies. |
D. Lappas; V. Argyriou; D. Makris; |
| 297 | Zero-Gradient Constraints for Destriping of Remote-Sensing Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes an effective and efficient destriping method for remote-sensing data. |
K. Naganuma; S. Takeyama; S. Ono; |
| 298 | Selection Based on Statistical Characteristics for Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a multi-scale sample selection based on statistical characteristics for object detection. |
Z. Li; Y. Yuan; D. Ma; |
| 299 | CSPN: Multi-Scale Cascade Spatial Pyramid Network for Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this problem and obtain better detection performance, we propose a novel net-work named Multi-Scale Cascade Spatial Pyramid Network (MS-CSPN) to strengthen Feature Pyramid. |
T. Wang; C. Ma; H. Su; W. Wang; |
| 300 | Dual-Stream Network Based On Global Guidance for Salient Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To remedy the problems, we propose a dual-stream network based on global guidance with two plug-ins, global attention based multi-scale high-level feature extraction module (GAMS) to mine global guidance and scale adaptive global guidance module (SAGG) to seamlessly integrate the global guidance into each decoding layer. |
S. Gao; Q. Guo; W. Zhang; W. Zhang; Z. Ji; |
| 301 | SSFENet: Spatial and Semantic Feature Enhancement Network for Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel network to address this dilemma, denoted as Spatial and Semantic Feature Enhancement Network (SSFENet). |
T. Wang; C. Ma; H. Su; W. Wang; |
| 302 | Saliency-Driven Versatile Video Coding for Neural Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we pro-pose such a saliency-driven coding framework for the video coding for machines task using the latest video coding standard Versatile Video Coding (VVC). |
K. Fischer; F. Fleckenstein; C. Herglotz; A. Kaup; |
| 303 | Object-Oriented Relational Distillation for Object Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose a novel Object-Oriented Relational Distillation (OORD) method that drives small detection models to have an effective performance like large detection models with constant efficiency. |
S. Miao; R. Feng; |
| 304 | Ensembling Object Detectors for Image and Video Data Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data. |
K. Chumachenko; J. Raitoharju; A. Iosifidis; M. Gabbouj; |
| 305 | Training Real-Time Panoramic Object Detectors with Virtual Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a panoramic virtual dataset for training object detectors on 360? images. |
Q. -Y. Shen; T. -G. Huang; P. -X. Ding; J. He; |
| 306 | Fast: Feature Aggregation for Detecting Salient Object in Real-Time Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper introduces a method named FAST for real-time salient object detection with an extremely efficient CNN architecture. |
L. Tang; B. Li; Y. Wu; B. Xiao; S. Ding; |
| 307 | Exploiting The Dual-Tree Complex Wavelet Transform for Ship Wake Detection in SAR Imagery Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we analyse synthetic aperture radar (SAR) images of the sea surface using an inverse problem formulation whereby Radon domain information is enhanced in order to accurately detect ship wakes. |
W. Ma; A. Achim; O. Karakus; |
| 308 | Task-Related Self-Supervised Learning For Remote Sensing Image Change Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, an unsupervised change detection method based on Task-related Self-supervised Learning Change Detection network with smooth mechanism(TSLCD) is proposed to eliminate it. |
Z. Cai; Z. Jiang; Y. Yuan; |
| 309 | Unsupervised Common Particular Object Discovery and Localization By Analyzing A Match Graph Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper describes an unsupervised method that more accurately discovers and localizes common particular objects within a set of images. |
M. Okuda; S. Satoh; Y. Sato; Y. Kidawara; |
| 310 | Predictive Coding for Lossless Dataset Compression Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We prove the equivalence of dataset compression to compressing a permutation-invariant structure of the data and implement such a scheme via predictive coding. |
M. Barowsky; A. Mariona; F. P. Calmon; |
| 311 | Adaptive Dual Tree Structure For Screen Content Coding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, adaptive dual tree structure is proposed in this paper wherein the coding structure of each coding tree unit is switched between separate and joint coding structure to adapt the textures adaptively. |
W. Zhu; J. Xu; L. Zhang; Y. Wang; |
| 312 | SNR-Adaptive Deep Joint Source-Channel Coding for Wireless Image Transmission Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Considering the problem of joint source-channel coding (JSCC) for multi-user transmission of images over noisy channels, an autoencoder-based novel deep joint source-channel coding scheme is proposed in this paper. |
M. Ding; J. Li; M. Ma; X. Fan; |
| 313 | Relying on A Rate Constraint to Reduce Motion Estimation Complexity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a rate-based candidate elimination strategy for Motion Estimation, which is considered one of the main sources of encoder complexity. |
G. B. Sant�Anna; L. Henrique Cancellier; I. Seidel; M. Grellert; J. L. G�ntzel; |
| 314 | A Novel Viewport-Adaptive Motion Compensation Technique for Fisheye Video Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel viewport-adaptive motion compensation technique that applies the motion vectors in different perspective viewports in order to realize these motion planes. |
A. Regensky; C. Herglotz; A. Kaup; |
| 315 | Rate-Distortion Optimized Motion Estimation for On-the-Sphere Compression of 360 Videos Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, the on-the-sphere compression [1] for omnidirectional still images is extended to videos. |
A. Marie; N. Mahmoudian Bidgoli; T. Maugey; A. Roumy; |
| 316 | Adaptive GOP Size Decision for Multi-Pass Video Coding Based on Hidden Markov Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a novel method to determine the size of each group of picture (GOP) using the multi-pass information is presented. |
B. Li; J. Han; Y. Xu; |
| 317 | Improved Intra Mode Coding Beyond Av1 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, two methods are proposed to further reduce the signaling cost of delta angles: cross-component delta angle coding, and context-adaptive delta angle coding, whereby the cross-component and spatial correlation of the delta angles are explored, respectively. |
Y. Jin; L. Zhao; X. Zhao; S. Liu; A. C. Bovik; |
| 318 | Decision Tree Based Inter Partition Termination For Av1 Encoding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To deal with this problem, in this paper, we propose a decision tree based algorithm to early terminate the inter prediction process by predicting splitting decisions at each depth. |
X. Chen; Y. Zhang; Y. Li; J. Wen; |
| 319 | Image Coding For Machines: An End-To-End Learned Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an image codec for machines which is neural network (NN) based and end-to-end learned. |
N. Le; H. Zhang; F. Cricri; R. Ghaznavi-Youvalari; E. Rahtu; |
| 320 | Sparse Flow Adversarial Model For Robust Image Compression Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel compression method called sparse flow adversarial model (SFAM). |
S. Zhao; S. Yang; Z. Liu; Z. Feng; X. Liu; |
| 321 | HVS-Based Perceptual Color Compression of Image Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel perceptual image coding technique, named Perceptual Color Compression (PCC). |
L. Prangnell; V. Sanchez; |
| 322 | HOCA: Higher-Order Channel Attention for Single Image Super-Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a higher-order channel attention (HOCA) module to enhance the representation ability of CNNs. |
Y. Lv; T. Dai; B. Chen; J. Lu; S. -T. Xia; J. Cao; |
| 323 | Image Super-Resolution Using Multi-Resolution Attention Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we propose a multi-resolution attention network (MRAN), which progressively reconstructs images at large scale factors by aggregating features from multiple resolutions. |
A. Liu; S. Li; Y. Chang; |
| 324 | Real Image Super-Resolution Using Token Based Contextual Attention Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate these issues, we propose a new token based attention module with innovative contextual encoding to enable SR models to be robust to image patch sizes at testing. |
Z. Pan; B. Li; |
| 325 | Feature Redundancy Mining: Deep Light-Weight Image Super-Resolution Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, by considering the correlation and redundancy of feature maps, we propose a feature information mining network to efficiently investigate the features, for the SISR problem. |
J. Xiao; W. Jia; K. -M. Lam; |
| 326 | Lightweight Non-Local Network for Image Super-Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we propose a lightweight non-local network (LNLN) for image super resolution in this paper. |
R. Wang; T. Lei; W. Zhou; Q. Wang; H. Meng; A. K. Nandi; |
| 327 | Lightweight and Accurate Single Image Super-Resolution with Channel Segregation Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose an efficient channel segregation block containing multiple branches with different depths, enabling the model to preserve basic content, and focusing on optimizing the detail content with fewer parameters. |
Z. -H. Niu; X. -P. Lin; A. -N. Yu; Y. -H. Zhou; Y. -B. Yang; |
| 328 | Deep Learning Architectural Designs for Super-Resolution Of Noisy Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to jointly perform denoising and super-resolution. |
A. Villar-Corrales; F. Schirrmacher; C. Riess; |
| 329 | Joint Coupled Transform Learning Framework for Multimodal Image Super-Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we model the cross-modal dependencies between different modalities for Multimodal Image Super-Resolution (MISR), i.e., enhance the Low Resolution (LR) image of target modality with the guidance of a High Resolution (HR) image from another modality. |
A. Gigie; A. A. Kumar; A. Majumdar; K. Kumar; M. G. Chandra; |
| 330 | Hyperspectral Image Super-Resolution Via Adjacent Spectral Fusion Strategy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we explore a new structure for hyperspectral image SR via adjacent spectral fusion strategy. |
Q. Li; Q. Wang; X. Li; |
| 331 | Raw Data Processing for Practical Time-of-Flight Super-Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we note that while attempting to address the last two issues, e. g., via burst mode, the lateral resolution can be effectively increased. |
M. H. Conde; |
| 332 | Edge-Aware Multi-Scale Progressive Colorization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these problems, we propose a novel edge-aware multi-scale progressive network (EMSPN). |
J. Xia; G. Tan; Y. Xiao; F. Xu; C. -S. Leung; |
| 333 | Learning Representation of Multi-Scale Object for Fine-Grained Image Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In our work, to extract more local features, we propose a method that could proposes multiple discriminative regions on different scales, which could provide more refined local and multi-sacle representation for fine-grained image retrieval. |
K. Sun; J. Zhu; |
| 334 | Super-Resolution and Infection Edge Detection Co-Guided Learning for Covid-19 Ct Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel super-resolution and infection edge detection co-guided learning network for COVID-19 CT segmentation (CogSeg). |
Y. Sang; J. Sun; S. Wang; H. Qi; K. Li; |
| 335 | Gating Feature Dense Network for Single Anisotropic Mr Image Super-Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a gating feature dense network to reconstruct HR MR images from low resolution acquisitions, where we use local residual dense block (LRDB) as the backbone. |
W. He; Y. Hu; L. Wang; Z. He; J. Du; |
| 336 | Adaptable Ensemble Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Adaptable Ensemble Distillation (AED) that inherits the merits of existing OKD methods while overcoming their major drawbacks. |
Y. Wang; D. Yang; W. Zhang; Z. Jiang; W. Zhang; |
| 337 | A Scale Invariant Measure of Flatness for Deep Network Minima Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we show that for deep networks with positively homogenous activations, these rescalings constitute equivalence relations, and that these equivalence relations induce a quotient manifold structure in the parameter space. |
A. Rangamani; N. H. Nguyen; A. Kumar; D. Phan; S. P. Chin; T. D. Tran; |
| 338 | Multi-Order Adversarial Representation Learning for Composed Query Image Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: So this paper proposes a new Multi-order Adversarial Network (MAN) which uses multilevel representations and simultaneously explores their low-order and high-order interactions, obtaining low-order and high-order features. |
Z. Fu; X. Chen; J. Dong; S. Ji; |
| 339 | Deep Neural Networks with Flexible Complexity While Training Based on Neural Ordinary Differential Equations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we experimentally investigate the effectiveness of using neural ordinary differential equations (NODEs) as a component to provide further depth to relatively shallower networks rather than stacked layers (depth) which achieved improvement with fewer parameters. |
Z. Luo; S. -i. Kamata; Z. Sun; W. Zhou; |
| 340 | Improving Memory Banks for Unsupervised Learning with Large Mini-Batch, Consistency and Hard Negative Mining Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce 3 improvements to the vanilla memory bank-based formulation which brings massive accuracy gains: (a) Large mini-batch: we pull multiple augmentations for each sample within the same batch and show that this leads to better models and enhanced memory bank updates. |
A. Bulat; E. S�nchez-Lozano; G. Tzimiropoulos; |
| 341 | Robust Binary Loss for Multi-Category Classification with Label Noise Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the problem, we propose to train deep models with robust binary loss functions. |
D. Liu; G. Yang; J. Wu; J. Zhao; F. Lv; |
| 342 | A Plug and Play Fast Intersection Over Union Loss for Boundary Box Regression Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we design a Fast Intersection over Union (FIoU) loss, which can not only keep the advantages but also solve the weakness of IoU-based losses. |
Z. Kuang; X. Fang; R. Zhang; X. Shao; H. Wang; |
| 343 | Attribute Decomposition for Flow-Based Domain Mapping Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To handle the mixed features for better generation, this paper presents an attribute decomposition based on the sequence data and carries out the flow-based image domain mapping. |
S. -J. Huang; J. -T. Chien; |
| 344 | Ada-Sise: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we combine both approaches as a hybrid visual explanation algorithm and propose an efficient interpretation method for convolutional neural networks. |
M. Sudhakar; S. Sattarzadeh; K. N. Plataniotis; J. Jang; Y. Jeong; H. Kim; |
| 345 | Network Pruning Using Linear Dependency Analysis on Feature Maps Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we regard a channel ?redundant? if its output is linearly dependent with respect to those of other channels. |
H. Pan; Z. Chao; J. Qian; B. Zhuang; S. Wang; J. Xiao; |
| 346 | Multiple-Input Multiple-Output Fusion Network for Generalized Zero-Shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a Multiple-Input Multiple-Output Fusion Network to GZSL. |
F. Zhong; G. Wang; Z. Chen; X. Yuan; F. Xia; |
| 347 | Representative Local Feature Mining for Few-Shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Given this, we propose a novel method that chooses representative local features to facilitate few-shot learning. |
K. Yan; L. Liu; J. Hou; P. Wang; |
| 348 | KAN: Knowledge-Augmented Networks for Few-Shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, considering that semantic information can enhance understanding when visual information is limited, we propose Knowledge-Augmented Networks (KAN), which combines the visual features with the semantic information extracted from knowledge graph to represent the features of each class. |
Z. Zhu; X. Lin; |
| 349 | Few-Shot Image Classification with Multi-Facet Prototypes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In particular, we propose an adaptive similarity measure, relying on predicted facet importance weights for a given set of categories. |
K. Yan; Z. Bouraoui; P. Wang; S. Jameel; S. Schockaert; |
| 350 | Self-Supervised Learning for Few-Shot Image Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for downstream tasks by learning from the data itself. |
D. Chen; Y. Chen; Y. Li; F. Mao; Y. He; H. Xue; |
| 351 | Domain Adaptation for Learning Generator From Paired Few-Shot Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a Paired Few-shot GAN (PFS-GAN) model for learning generators with sufficient source data and a few target data. |
C. -C. Teng; P. -Y. Chen; W. -C. Chiu; |
| 352 | Deep Semi-Supervised Metric Learning Via Identification of Manifold Memberships Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a method which allows the use of class-representative anchors (proxies), and avoids the computational costs associated with triplet sampling. |
F. Zhuang; P. Moulin; |
| 353 | A Ranked Similarity Loss Function with Pair Weighting for Deep Metric Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these problems, we propose to build a ranked similarity loss function with pair weighting (dubbed RMS loss). |
J. Wang; Z. Zhang; D. Huang; W. Song; Q. Wei; X. Li; |
| 354 | Statistical Distance Metric Learning for Image Set Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we obviate the need of feature aggregation and propose a novel Statistical Distance Metric Learning (SDML) framework, which represents each image set as a probability distribution in embedding feature space and compares two image sets by statistical distance between their distributions. |
T. -Y. Hu; A. G. Hauptmann; |
| 355 | Distribution-Aware Hierarchical Weighting Method for Deep Metric Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose distribution-aware hierarchical weighting (DHW) method for deep metric learning. |
Y. Zhu; Y. Feng; M. Zhou; B. Qiang; L. Hou U; J. Zhu; |
| 356 | Integrated Grad-Cam: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks Via Integrated Gradient-Based Scoring Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Addressing this problem, we introduce a solution to tackle this issue by computing the path integral of the gradient-based terms in Grad-CAM. |
S. Sattarzadeh; M. Sudhakar; K. N. Plataniotis; J. Jang; Y. Jeong; H. Kim; |
| 357 | Visualizing Association in Exemplar-Based Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this problem, we propose a novel method of explainable classification; this method uses images representing each image class, which we call exemplars. |
T. Kashima; R. Hataya; H. Nakayama; |
| 358 | HFGCNET: High-Frequency Graph Reasoning for Finer Semantic Image Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work presents a high-frequency graph convolution operation to solve the above problems. |
Z. Sun; R. Wang; Z. Luo; W. Chen; |
| 359 | Unsupervised Image Segmentation with Spatial Triplet Markov Trees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this article we study an extension of HMTs called Spatial Triplet Markov Trees (STMTs) which is designed to greatly increase the correlations of the random variables while keeping the possibility of direct and exact inference procedures. |
H. Gangloff; J. -B. Courbot; E. Monfrini; C. Collet; |
| 360 | Cross Scene Video Foreground Segmentation Via Co-Occurrence Probability Oriented Supervised and Unsupervised Model Interaction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a cross scene video foreground segmentation framework to extend the generalization capability of those supervised model depending on scene-specific training. |
D. Liang; B. Kang; X. Liu; H. Sun; L. Zhang; N. Liu; |
| 361 | Instance Segmentation with The Number of Clusters Incorporated in Embedding Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to embed prior clustering information into an embedding learning framework FCRNet, stimulating the one-stage instance segmentation. |
J. Cao; H. Yan; |
| 362 | Decouple The High-Frequency and Low-Frequency Information of Images for Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: At present, the semantic segmentation methods are all based on CNN and ignore the advantages of traditional image processing technology. We combine the two and make them promote each other. |
L. Shan; X. Li; W. Wang; |
| 363 | MPDNet: A 3D Missing Part Detection Network Based on Point Cloud Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle the issue, in this paper, we propose a novel model named MPDNet, which exploits 3D point cloud pairs as input for missing part detection. |
Z. Fan; H. Liu; J. He; M. Zhang; X. Du; |
| 364 | SM+: Refined Scale Match for Tiny Person Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the scale alignment between pre-training and target datasets, and propose a new refined Scale Match method (termed SM+) for tiny person detection. |
N. Jiang; X. Yu; X. Peng; Y. Gong; Z. Han; |
| 365 | Sub-Band Grouping Spectral Feature-Attention Block for Hyperspectral Image Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed a novel sub-band grouping recurrent neural network (RNN) model with gated recurrent units (GRUs) to find the intrinsic feature in spectral information. |
W. Zhou; S. -i. Kamata; Z. Luo; |
| 366 | Unsupervised Stacked Capsule Autoencoder for Hyperspectral Image Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on HSI data’s aforementioned structural characteristics, combined with the stacked capsule autoencoder, we propose our model to achieve an unsupervised HSI classification. |
E. Pan; Y. Ma; X. Mei; F. Fan; J. Ma; |
| 367 | Robust Graph Autoencoder for Hyperspectral Anomaly Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to tackle these problems, we propose a robust graph autoencoder (RGAE) for hyperspectral anomaly detection. |
G. Fan; Y. Ma; J. Huang; X. Mei; J. Ma; |
| 368 | Reflectance-Oriented Probabilistic Equalization for Image Enhancement Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this problem, we propose a novel 2D histogram equalization approach. |
X. Wu; Y. Sun; A. Kimura; K. Kashino; |
| 369 | PD-GAN: Perceptual-Details GAN for Extremely Noisy Low Light Image Enhancement Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve the problems, we pro-pose perceptual-details GAN (PD-GAN) utilizing Zero-DCE to initially recover illumination and combine residual dense-block Encoder-Decoder structure to suppress noise while finely adjusting the illumination. |
Y. Liu; Z. Wang; Y. Zeng; H. Zeng; D. Zhao; |
| 370 | Heterogeneous Two-Stream Network with Hierarchical Feature Prefusion for Multispectral Pan-Sharpening Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a heterogeneous two-stream network (HTSNet) with hierarchical feature prefusion for MS pan-sharpening. |
D. Wang; Y. Bai; B. Bai; C. Wu; Y. Li; |
| 371 | Synergic Feature Attention for Image Restoration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome these problems, in this paper, we propose a novel Synergic Attention Network (SAT-Net) for image restoration as an inventive attempt to combine local and non-local attention mechanisms to restore complex textures and highly repetitive details distinguishingly. |
C. Mou; J. Zhang; |
| 372 | Efficient Multi-Objective GANs for Image Restoration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we propose an efficient formulation of multiple loss components for training GANs. |
J. Su; H. Yin; |
| 373 | Self-Convolution: A Highly-Efficient Operator for Non-Local Image Restoration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel Self-Convolution operator to exploit image non-local similarity in a self-supervised way. |
L. Guo; Z. Zha; S. Ravishankar; B. Wen; |
| 374 | NMF-SAE: An Interpretable Sparse Autoencoder for Hyperspectral Unmixing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we combine the advantages of both model-based and learning-based methods and propose a nonnegative matrix factorization (NMF) inspired sparse autoencoder (NMF-SAE) for hyperspectral unmixing. |
F. Xiong; J. Zhou; M. Ye; J. Lu; Y. Qian; |
| 375 | An ADMM Based Network for Hyperspectral Unmixing Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we use algorithm unrolling approaches in order to design a new neural network structure applicable to hyperspectral unmixing challenges. |
C. Zhou; M. R. D. Rodrigues; |
| 376 | Variational Autoencoders for Hyperspectral Unmixing with Endmember Variability Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a variational autoencoder (VAE) framework for hyperspectral unmixing accounting for the endmember variability. |
S. Shi; M. Zhao; L. Zhang; J. Chen; |
| 377 | Augmented Gaussian Linear Mixture Model for Spectral Variability in Hyperspectral Unmixing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel hyperspectral unmixing through the perturbed linear mixture model to take into account the spectral variability offset of the linear mixture model. |
Y. E. Salehani; E. Arabnejad; S. Gazor; |
| 378 | UTDN: An Unsupervised Two-Stream Dirichlet-Net for Hyperspectral Unmixing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel two-stream Dirichlet-net, termed as uTDN, to address the above problems. |
Q. Jin; Y. Ma; X. Mei; H. Li; J. Ma; |
| 379 | Laplacian Regularized Tensor Low-Rank Minimization for Hyperspectral Snapshot Compressive Imaging Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a tensor-based low-rank reconstruction algorithm with hyper-Laplacian constraint for hyperspectral SCI systems. |
Y. Yang; F. Jiang; H. Lu; |
| 380 | Compressing Local Descriptor Models for Mobile Applications Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider these practical aspects and improve the state-of-the-art HardNet model through the use of depthwise separable layers and an efficient tensor decomposition. |
R. Miles; K. Mikolajczyk; |
| 381 | VK-Net: Category-Level Point Cloud Registration with Unsupervised Rotation Invariant Keypoints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose VK-Net, a neural network that learns to discover a set of category-specific keypoints from a single point cloud in an unsupervised manner. |
Z. Chen; W. Yang; Z. Xu; Z. Shi; L. Huang; |
| 382 | Matching As Color Images: Thermal Image Local Feature Detection and Description Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by this challenge, we propose a triplet based Siamese CNN for feature detection and extraction for any given thermal image. |
B. Deshpande; S. Hanamsheth; Y. Lu; G. Lu; |
| 383 | Frame Rate Up-Conversion Using Key Point Agnostic Frequency-Selective Mesh-to-Grid Resampling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We use the model-based key point agnostic frequency-selective mesh-to-grid resampling (AFSMR) for this task and show that AFSMR works best for applications that contain irregular meshes with varying densities. |
V. Heimann; A. Spruck; A. Kaup; |
| 384 | Efficient Real-Time Video Stabilization with A Novel Least Squares Formulation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel video stabilization algorithm (LSstab) that removes unwanted motions in real-time. |
J. Ke; A. J. Watras; J. -J. Kim; H. Liu; H. Jiang; Y. H. Hu; |
| 385 | Decomposing Textures Using Exponential Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a new approach using a recent 2-dimensional exponential analysis technique. |
Y. Hou; A. Cuyt; W. -s. Lee; D. Bhowmik; |
| 386 | G-Arrays: Geometric Arrays for Efficient Point Cloud Processing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel data structure for representing point clouds with a reduced memory requirement and a faster lookup than the state-of-the-art formats. |
H. Roodaki; M. Dehyadegari; M. N. Bojnordi; |
| 387 | QoE-Driven and Tile-Based Adaptive Streaming for Point Clouds Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this, we propose a QoE-driven and tile-based adaptive streaming approach for point clouds, to reduce transmission redundancy and maximize user?s QoE. |
L. Wang; C. Li; W. Dai; J. Zou; H. Xiong; |
| 388 | Dynamic Point Cloud Compression Using A Cuboid Oriented Discrete Cosine Based Motion Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: An improved commonality modeling technique is proposed that employs discrete cosine basis oriented motion models and the domains of such models are approximated by homogeneous regions called cuboids. |
A. Ahmmed; M. Paul; M. Murshed; D. Taubman; |
| 389 | An Adaptive Pyramid Single-View Depth Lookup Table Coding Method Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, an adaptive pyramid single-view depth lookup table coding method is proposed, with the purpose of designing a clean syntax structure in the sequence header with reasonably good performance. |
Y. Cai; R. Wang; S. Gu; J. Zhang; W. Gao; |
| 390 | Patch Decoder-Side Depth Estimation In Mpeg Immersive Video Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a new approach for achieving bitrate and pixel rate reduction in the MPEG immersive video coding setting. |
M. Milovanovic; F. Henry; M. Cagnazzo; J. Jung; |
| 391 | Geometry Consistency Of Augmented Reality Based On Semantics Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In augmented reality, for achieving geometric consistency in the perspective projection virtual-real, we propose a semantic consistency method to achieve the fusion between virtual and real objects with selected segmented objects in the real scene as references. |
H. Quan; M. Yao; X. Qian; |
| 392 | What And Where To Focus In Person Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For these two findings, we first propose multilevel semantic aggregation algorithm for more discriminative feature descriptors. Then, a pose-assisted attention module is designed to highlight fine-grained area of the target and simultaneously capture valuable clues for identification. |
T. Zhou; K. Tian; |
| 393 | Stable and Effective One-Step Method for Person Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose an end-to-end model containing the feature extractor, the region proposal network, and the multi-task learning module. |
N. Lv; X. Xiang; X. Wang; J. Yang; R. Abdeen; A. El Saddik; |
| 394 | An Adaptive Part-Based Model For Person Re-Identification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the part-misalignment problem and learn a more discriminative embedding for person Re-ID, we propose a novel Adaptive Part-based Model (APM), which adaptively partition the extracted feature maps by a Partition-Aware module to learn an embedding. |
X. -P. Lin; Y. -B. Yang; |
| 395 | Crowd Counting Via Multi-Level Regression With Latent Gaussian Maps Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, a novel end-to-end crowd counting framework via multi-level regression with latent Gaussian maps is proposed, which is consisted of GaussianNet, EstimateNet and Discriminator. |
Y. Gao; H. Yang; |
| 396 | Lightweight Dual-Task Networks For Crowd Counting In Aerial Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, this paper proposes a lightweight dual-task network (LDNet) for crowd counting, which only uses bifurcated structure to overcome these new challenges in aerial images without complicated pipelines. |
Y. Tian; C. Duan; R. Zhang; Z. Wei; H. Wang; |
| 397 | SANet++: Enhanced Scale Aggregation with Densely Connected Feature Fusion for Crowd Counting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present SANet++ with a novel architecture to generate high-quality density maps and further perform accurate counting. |
S. Pan; Y. Zhao; F. Su; Z. Zhao; |
| 398 | Attentive Semantic Exploring for Manipulated Face Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we propose a novel manipulated face detection method based on Multilevel Facial Semantic Segmentation and Cascade Attention Mechanism. |
Z. Chen; H. Yang; |
| 399 | Efficient Face Manipulation Via Deep Feature Disentanglement And Reintegration Net Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a novel Feature Disentanglement and Reintegraion network (FDRNet), which employs ground-truth images as informative supervision and dynamically adapts the fusion of informative features of the ground-truth images effectively and efficiently. |
B. Cheng; T. Dai; B. Chen; S. Xia; X. Li; |
| 400 | Continuous Face Aging Generative Adversarial Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose the continuous face aging generative adversarial networks (CFA-GAN). |
S. Jeon; P. Lee; K. Hong; H. Byun; |
| 401 | Fast Inverse Mapping of Face GANs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We train a ResNet architecture to map given faces to latent vectors that can be used to generate faces nearly identical to the target. |
N. Bayat; V. R. Khazaie; Y. Mohsenzadeh; |
| 402 | Multi-Level Adaptive Region of Interest and Graph Learning for Facial Action Unit Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel multi-level adaptive ROI and graph learning (MARGL) framework to tackle this problem. |
J. Yan; B. Jiang; J. Wang; Q. Li; C. Wang; S. Pu; |
| 403 | Bridging Unpaired Facial Photos and Sketches By Line-Drawings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel method to learn face sketch synthesis models by using unpaired data. |
M. Shang; F. Gao; X. Li; J. Zhu; L. Dai; |
| 404 | Temporal Rain Decomposition with Spatial Structure Guidance for Video Deraining Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a multi-frame deraining network with temporal rain decomposition and spatial structure guidance to more effectively accomplish video deraining. |
X. Xue; Y. Ding; L. Ma; Y. Wang; R. Liu; X. Fan; |
| 405 | GTA-Net: Gradual Temporal Aggregation Network for Fast Video Deraining Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, to effectively exploit temporal information, we develop a simple but effective network, Gradual Temporal Aggregation Network (GTA-Net for short). |
X. Xue; X. Meng; L. Ma; R. Liu; X. Fan; |
| 406 | Dense Feature Pyramid Grids Network for Single Image Deraining Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel densely connected network with Dense Feature Pyramid Grids Modules, called DFPGN, to solve the rain removal task. |
Z. Wang; C. Wang; Z. Su; J. Chen; |
| 407 | A Fast and Efficient Network for Single Image Deraining Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, we propose a novel Adaptive Dilated Network (ADN) to remove rain streaks from a single image while using less parameters and running faster than previous methods. |
Y. Yang; H. Lu; |
| 408 | DNANet: Dense Nested Attention Network for Single Image Dehazing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an innovative approach, called Dense Nested Attention Network (DNANet), to directly restore a clear image from a hazy image with a new topology of connection paths. |
D. Ren; J. Li; M. Han; M. Shu; |
| 409 | FWB-Net: Front White Balance Network for Color Shift Correction in Single Image Dehazing Via Atmospheric Light Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Bearing this in mind, in this study, first, a new non-homogeneous atmospheric scattering model (NH-ASM) is proposed for improving image modeling of hazy images taken under complex illumination conditions. Second, a new U-Net based front white balance module (FWB-Module) is dedicatedly designed to correct color shift before generating dehazing result via atmospheric light estimation. |
C. Wang; Y. Huang; Y. Zou; Y. Xu; |
| 410 | Learning Integrodifferential Models for Image Denoising Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce an integrodifferential extension of the edge-enhancing anisotropic diffusion model for image denoising. |
T. Alt; J. Weickert; |
| 411 | Unrolling of Deep Graph Total Variation for Image Denoising Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we combine classical graph signal filtering with deep feature learning into a competitive hybrid design?one that utilizes interpretable analytical low-pass graph filters and employs 80% fewer network parameters than state-of-the-art DL denoising scheme DnCNN. |
H. Vu; G. Cheung; Y. C. Eldar; |
| 412 | Learning Model-Blind Temporal Denoisers Without Ground Truths Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a general framework for temporal denoising that successfully addresses these challenges. |
Y. Li; B. Guo; J. Wen; Z. Xia; S. Liu; Y. Han; |
| 413 | Image Denoising Based on Correlation Adaptive Sparse Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper aims to fully exploit local and non-local correlation of image contents separately so that near-optimal sparse representations are achieved and thus the uncertainty of signals is minimized. |
H. Liu; J. Zhang; C. Mou; |
| 414 | NASA: A Noise-Adaptive and Structure-Aware Learning Framework for Image Deblurring Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To settle this issue, we develop a Noise-Adaptive Structure-Aware learning framework (NASA) to achieve fully intelligent manufacturing. |
X. Liu; L. Ma; R. Liu; W. Zhong; X. Fan; Z. Luo; |
| 415 | Multiple Auxiliary Networks for Single Blind Image Deblurring Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Multiple Auxiliary Networks (MANet) for single blind image deblurring to assist norm L1-loss function and enhance the quality of the deblurring image. |
C. Li; Q. Wang; S. Liu; X. Li; |
| 416 | Joint Learning of Image Aesthetic Quality Assessment and Semantic Recognition Based on Feature Enhancement Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore the relationships between aesthetic quality assessment and semantic recognition task, and employ a multi-task convolutional neural network with feature enhancement mechanism to effectively integrate these two tasks. |
X. Liu; X. Nie; Z. Shen; Y. Yin; |
| 417 | Nested Error Map Generation Network for No-Reference Image Quality Assessment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a multi-task learning neural network for No-Reference image quality assessment (NR-IQA). |
J. Chen; H. Wang; G. Li; S. Liu; |
| 418 | Regression or Classification? New Methods to Evaluate No-reference Picture and Video Quality Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To make the problem more tractable, we propose two new methods – binary, and ordinal classification – as alternatives to evaluate and compare no-reference quality models at coarser levels. |
Z. Tu; et al. |
| 419 | Blind Image Quality Evaluator with Scale Robustness Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a more generalized blind image quality evaluator with scale robustness (BIQESR) to assess image quality by locating the robust feature points in a multi-scale space. |
C. Wang; M. Li; |
| 420 | Multi-Scale Feature-Guided Stereoscopic Video Quality Assessment Based on 3d Convolutional Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed a multi-scale feature-guided 3D convolutional neural network for SVQA which not only use 3D convolution to capture spatio-temporal features but also aggregate multi-scale information by a new multi-scale unit. |
Y. Feng; S. Li; Y. Chang; |
| 421 | No-Reference Stereoscopic Image Quality Assessment Based on The Human Visual System Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Recently witnessed the significant progress of biotechnology and motivated by the deeper research on the HVS, we take a step to bridge the gap between HVS and SIQA by generalizing the optic chiasm algorithm and introducing biological vision fusion mechanism in our work. |
F. Meng; S. Li; Y. Chang; |
| 422 | Stereo Rectification Based on Epipolar Constrained Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a novel deep neural network-based method for stereo image rectification. |
Y. Wang; Y. Lu; G. Lu; |
| 423 | Multi-Scale Cascade Disparity Refinement Stereo Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, this paper presents MCDRNet, which combines traditional methods with neural networks to achieve real-time and accurate stereo matching results. |
X. Jia; et al. |
| 424 | Hierarchical Context Guided Aggregation Network for Stereo Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a simple yet efficient network named Hierarchical Context Guided Aggregation Network (HCGANet). |
J. Peng; W. Xie; Z. Huang; W. Chen; Y. Zhao; |
| 425 | Cost Affinity Learning Network for Stereo Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel cost affinity learning network(CAL-Net) whose Affinity Enhanced Module(AEM) extracts the affinity of the elements in the cost feature and reconstructs a more discriminative feature. |
S. Chen; B. Li; W. Wang; H. Zhang; H. Li; Z. Wang; |
| 426 | Video Quality Prediction Using Voxel-Wise FMRI Models of The Visual Cortex Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we address the problem of full-reference video quality prediction. |
N. S. Mahankali; S. S. Channappayya; |
| 427 | Tensor Decomposition Via Core Tensor Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an efficient TD algorithm that aims to learn a global mapping from input tensors to latent core tensors, under the assumption that the mappings of multiple tensors might be shared or highly correlated. |
J. ZHANG; Z. TAO; L. ZHANG; Q. ZHAO; |
| 428 | Sign Language Segmentation with Temporal Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: The objective of this work is to determine the location of temporal boundaries between signs in continuous sign language videos. |
K. Renz; N. C. Stache; S. Albanie; G. Varol; |
| 429 | An Adaptive Discriminant and Sparsity Feature Descriptor for Finger Vein Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an adaptive discriminant and sparsity feature descriptor (DSFD) for FV feature extraction and recognition. |
S. Li; B. Zhang; |
| 430 | Routinggan: Routing Age Progression and Regression with Disentangled Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these deficiencies and have the best of both worlds, this paper introduces a dropout-like method based on GAN (RoutingGAN) to route different effects in a high-level semantic feature space. |
Z. Huang; J. Zhang; H. Shan; |
| 431 | Semantic-Aware Unpaired Image-to-Image Translation for Urban Scene Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, in this paper, we reasonably modify the previous problem setting and present a novel semantic-aware method. |
Z. Li; R. Togo; T. Ogawa; M. Haseyama; |
| 432 | Fontnet: On-Device Font Understanding and Prediction Pipeline Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose two engines: Font Detection Engine, which identifies the font style, color and size attributes of text in an image and a Font Prediction Engine, which predicts similar fonts for a query font. |
R. S; R. Khurana; V. Agarwal; J. R. Vachhani; G. Bhanodai; |
| 433 | Agent-Environment Network for Temporal Action Proposal Generation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on the action definition that a human, known as an agent, interacts with the environment and performs an action that affects the environment, we propose a contextual Agent-Environment Network. |
V. -K. Vo-Ho; N. Le; K. Kamazaki; A. Sugimoto; M. -T. Tran; |
| 434 | Adaptive Multi-Domain Learning for Outdoor 3d Human Pose and Shape Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we first point out this problem and then address it via a novel cascade multi-domain learning module (CMDL), where multiple adapters are employed to extract more discriminative features for different domains. |
Z. Gui; S. Zhang; K. Wang; J. Yang; P. C. Yuen; |
| 435 | Lightweight Human Pose Estimation Under Resource-Limited Scenes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the problem of lightweight human pose estimation under resource-limited scenes.We first redesign a lightweight bottleneck block with two concepts: depthwise convolution and attention mechanism. |
Z. Zhang; J. Tang; G. Wu; |
| 436 | Absolute 3d Pose Estimation and Length Measurement of Severely Deformed Fish from Monocular Videos in Longline Fishing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Unlike related works, which either require expensive 3D ground-truth data and/or multiple-view images to provide depth information, or are limited to rigid objects, we propose a novel frame-based method to estimate the absolute 3D fish pose and fish length from a single-view 2D segmentation mask. |
J. Mei; J. -N. Hwang; S. Romain; C. Rose; B. Moore; K. Magrane; |
| 437 | Camera Calibration with Pose Guidance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To resolve above issues, we propose a calibration system called Calibration with Pose Guidance to improve calibration accuracy, reduce calibration variance among different users or different trials of the same person. |
Y. Ren; F. Hu; |
| 438 | Real Versus Fake 4k – Authentic Resolution Assessment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This work aims at authentic resolution assessment (ARA). |
R. R. Shah; V. Anirudh Akundy; Z. Wang; |
| 439 | Perceptual Quality Assessment for Recognizing True and Pseudo 4k Content Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To meet the imperative demand for monitoring the quality of Ultra High-Definition (UHD) content in multimedia industries, we propose an efficient no-reference (NR) image quality assessment (IQA) metric to distinguish original and pseudo 4K contents and measure the quality of their quality in this paper. |
W. Zhu; G. Zhai; X. Min; X. Yang; X. -P. Zhang; |
| 440 | A New Tubular Structure Tracking Algorithm Based On Curvature-Penalized Perceptual Grouping Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new minimal path-based framework for minimally interactive tubular structure tracking in conjunction with a perceptual grouping scheme. |
L. Liu; D. Chen; M. Shu; H. Shu; L. D. Cohen; |
| 441 | Multiple Human Tracking in Non-Specific Coverage with Wearable Cameras Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, in this paper we propose a Markov Decision Process with jump state (JMDP) to model the target?s lifetime in tracking, and use optical flow of the camera motion and the statistical information of the targets to model the camera state transition. |
S. Wang; R. Han; W. Feng; S. Wang; |
| 442 | Fine-Grained Pose Temporal Memory Module for Video Pose Estimation and Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To better solve these problems and utilize the temporal information efficiently and effectively, we present a novel structure, called pose temporal memory module, which is flexible to be transferred into top-down pose estimation frameworks. |
C. WANG; et al. |
| 443 | Drawing Order Recovery from Trajectory Components Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on the idea that drawing trajectories are able to be recovered by connecting their trajectory components in correct orders, this work proposes a novel DOR method from static images. |
M. Yang; X. Zhou; Y. Sun; J. Chen; B. Qiang; |
| 444 | Deep Hashing for Motion Capture Data Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose an efficient retrieval method for human motion capture (MoCap) data based on supervised deep hash code learning. |
N. Lv; Y. Wang; Z. Feng; J. Peng; |
| 445 | Hierarchical Attention Fusion for Geo-Localization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we cast the geo-localization as a 2D image retrieval task. |
L. Yan; Y. Cui; Y. Chen; D. Liu; |
| 446 | AttentionLite: Towards Efficient Self-Attention Models for Vision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel framework for producing a class of parameter and compute efficient models called AttentionLite suitable for resource constrained applications. |
S. Kundu; S. Sundaresan; |
| 447 | Attention-Guided Second-Order Pooling Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To handle above limitations, this paper proposes a novel attention-guided second-order pooling convolutional network (ASP-Net). |
S. Chen; Q. Sun; C. Li; J. Zhang; Q. Zhang; |
| 448 | SA-Net: Shuffle Attention for Deep Convolutional Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an efficient Shuffle Attention (SA) module to address this issue, which adopts Shuffle Units to combine two types of attention mechanisms effectively. |
Q. -L. Zhang; Y. -B. Yang; |
| 449 | An Attention Based Wavelet Convolutional Model for Visual Saliency Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, an attention based Wavelet Convolutional Neural Network (WCNN) is implemented that efficiently extracts the spatial, spectral and semantic features of the image in multiple resolution and it turns out to be suitable for locating the visually salient regions. |
R. S. Bhooshan; S. K; |
| 450 | Cascade Attention Fusion for Fine-Grained Image Captioning Based on Multi-Layer LSTM Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a visual and semantic fusion network with a margin-based training guidance mechanism to generate fine image descriptions that depict more objects, attributes and other distinguishing aspects of images. |
S. Wang; et al. |
| 451 | Webly Supervised Deep Attentive Quantization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this problem, we propose a novel method termed Webly Supervised Deep Attentive Quantization (WSDAQ), where deep quantization is trained on web images associated with some userprovided weak tags, without consulting any ground-truth labels. |
J. Wang; B. Chen; T. Dai; S. -T. Xia; |
| 452 | Unsupervised Audio-Visual Subspace Alignment for High-Stakes Deception Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose the first multimodal unsupervised transfer learning approach that detects real-world, high-stakes deception in videos with-out using high-stakes labels. |
L. MATHUR; M. J. MATARIc; |
| 453 | Violence Detection in Videos Based on Fusing Visual and Audio Information Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We proposed a neural network containing three modules for fusing multimodal information: 1) attention module for utilizing weighted features to generate effective features based on the mutual guidance between visual and audio information; 2) fusion module for integrating features by fusing visual and audio information based on the bilinear pooling mechanism; and 3) mutual Learning module for enabling the model to learn visual information from another neural network with a different architecture. |
W. -F. Pang; Q. -H. He; Y. -j. Hu; Y. -X. Li; |
| 454 | QUERYD: A Video Dataset with High-Quality Text and Audio Narrations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce QuerYD, a new large-scale dataset for retrieval and event localisation in video. |
A. -M. Oncescu; J. F. Henriques; Y. Liu; A. Zisserman; S. Albanie; |
| 455 | Generating Natural Questions from Images for Multimodal Assistants Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present an approach for generating diverse and meaningful questions that consider image content and metadata of image (e.g., location, associated keyword). |
A. Patel; A. Bindal; H. Kotek; C. Klein; J. Williams; |
| 456 | An Adaptive Multi-Scale and Multi-Level Features Fusion Network with Perceptual Loss for Change Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a novel adaptive multi-scale and multi-level features fusion network for change detection in very-high-resolution bi-temporal remote sensing images. |
J. Xu; Y. Luo; X. Chen; C. Luo; |
| 457 | SeeHear: Signer Diarisation and A New Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a framework to collect a large-scale, diverse sign language dataset that can be used to train automatic sign language recognition models.The first contribution of this work is SDTrack, a generic method for signer tracking and diarisation in the wild. |
S. Albanie; et al. |
| 458 | Semantic Image Synthesis from Inaccurate and Coarse Masks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose a smoothing method, which we call local label smoothing (LLS), that incorporates label smoothing per small patch of an input mask to learn mapping from masks to images even when semantic masks are inaccurate. |
K. Katsumata; H. Nakayama; |
| 459 | Range Guided Depth Refinement and Uncertainty-Aware Aggregation for View Synthesis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a framework of view synthesis, including range guided depth refinement and uncertainty-aware aggregation based novel view synthesis. |
Y. Chang; Y. Chen; G. Wang; |
| 460 | DP-VTON: Toward Detail-Preserving Image-Based Virtual Try-on Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To resolve this issue, we present a novel virtual try-on network, DP-VTON. |
Y. Chang; et al. |
| 461 | Light Field Style Transfer with Local Angular Consistency Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel optimization-based method for light field style transfer which iteratively propagates the style from the centre view towards the outer views while enforcing local angular consistency. |
D. Egan; M. Alain; A. Smolic; |
| 462 | Skip Attention GAN for Remote Sensing Image Synthesis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We establish Skip Attention Mechanism to deal with this problem, which learns dependencies between local points on low-resolution feature maps, and then upsample the attention map and combine it with high-resolution feature maps. |
K. Deng; K. Zhang; P. Yao; S. Cheng; P. He; |
| 463 | Image Generation Based on Texture Guided VAE-AGAN for Regions of Interest Detection in Remote Sensing Images Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To cope with this issue, we propose a novel method based on texture guided variational autoencoder-attention wise generative adversarial network (VAE-AGAN) to augment the training data for ROI detection. |
L. Zhang; Y. Liu; |
| 464 | EADNet: Efficient Asymmetric Dilated Network For Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an efficient asymmetric dilated semantic segmentation network, named EADNet, which consists of multiple developed asymmetric convolution branches with different dilation rates to capture the variable shapes and scales information of an image. |
Q. Yang; T. Chen; J. Fan; Y. Lu; C. Zuo; Q. Chi; |
| 465 | Ltaf-Net: Learning Task-Aware Adaptive Features and Refining Mask for Few-Shot Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we propose a novel model named LTAF-Net for few-shot segmentation. |
B. Mao; L. Wang; S. Xiang; C. Pan; |
| 466 | Cgan-Net: Class-Guided Asymmetric Non-Local Network for Real-Time Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a Class-Guided Asymmetric Non-local Network (CGAN-Net) to enhance the class-discriminability in learned feature map, while maintaining real-time efficiency. |
H. Chen; Q. Hu; J. Yang; J. Wu; Y. Guo; |
| 467 | Aggregation Architecture and All-to-one Network for Real-Time Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we make the following contributions: (i) First, unlike the previous three architectures, we propose a new aggregation architecture as the network back-bone. (ii) Second, a multi-level auxiliary loss design model is used for the training phase, which can improve the model segmentation effect. (iii) According to this aggregation structure, an all-to-one network (ATONet) for real-time semantic segmentation is proposed, which achieves a good trade-off between speed and accuracy by assembling the features of all blocks. |
K. CAO; X. HUANG; J. SHAO; |
| 468 | Nlkd: Using Coarse Annotations For Semantic Segmentation Based on Knowledge Distillation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a noise learning framework based on knowledge distillation NLKD, to improve segmentation performance on unclean data. |
D. Liang; Y. Du; H. Sun; L. Zhang; N. Liu; M. Wei; |
| 469 | Knowledge Reasoning for Semantic Segmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome the limitation of the traditional method, we propose a Knowledge Reasoning Net (KRNet) that consists of two crucial modules: (1) a prior knowledge mapping module that incorporates external knowledge by graph convolutional network to guide learning semantic representations and (2) a knowledge reasoning module that correlates these representations with a graph built on the external knowledge and explores their interactions via the knowledge reasoning. |
S. Chen; Z. Li; X. Yang; |
| 470 | Non-Convex Sparse Deviation Modeling Via Generative Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, the generative model is used to introduce the structural properties of the signal to replace the common sparse hypothesis, and a non-convex compressed sensing sparse deviation model based on the generative model (lq-Gen) is proposed. |
Y. Yang; H. Wang; H. Qiu; J. Wang; Y. Wang; |
| 471 | Imrnet: An Iterative Motion Compensation and Residual Reconstruction Network for Video Compressed Sensing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes an iterative motion compensation and residual reconstruction network for VCS, called ImrNet. |
X. Yang; C. Yang; |
| 472 | Deep Color Constancy Using Temporal Gradient Under Ac Light Sources Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: While most of conventional methods focus on only spatial information of a single image, we propose a deep spatio-temporal color constancy method. |
J. -W. HA; J. -S. YOO; J. -O. KIM; |
| 473 | End-to-End Learning of Variational Models and Solvers for The Resolution of Interpolation Problems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We consider an application to inverse problems with incomplete datasets (image inpainting and multivariate time series interpolation). |
R. Fablet; L. Drumetz; F. Rousseau; |
| 474 | Multi-Models Fusion for Light Field Angular Super-Resolution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, therefore, we propose a multi-models fusion for LF SR in angular domain. |
F. Cao; P. An; X. Huang; C. Yang; Q. Wu; |
| 475 | Hide Chopin in The Music: Efficient Information Steganography Via Random Shuffling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore the room introduced by the low-rank property of natural signals (i.e., images, audios), and propose a training-free model for efficient information steganography, which provides a capacity of hiding full-size images into carriers of the same spatial resolution. |
Z. Sun; C. Li; Q. Zhao; |
| 476 | Pointer Networks for Arbitrary-Shaped Text Spotting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a highly efficient one-stage method named PointerNet for arbitrary-shaped text spotting. |
Y. Zhang; W. Yang; Z. Xu; Y. Li; Z. Chen; L. Huang; |
| 477 | Rotation Invariance Analysis of Local Convolutional Features in Image Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, our objective is to enhance the robustness of LC features against image rotation. |
L. Zhao; Y. Wang; J. Kato; |
| 478 | Signature Feature Marking Enhanced IRM Framework for Drone Image Analysis in Precision Agriculture Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we are proposing enhancement to Invariant Risk Minimization (IRM) framework which is Signature Feature Marking (SFM) enhanced IRM for object classification. |