Every paper, ordered by impact or potential impact. About MM 2024
| ID | Paper | Authors | Source | Date |
|---|---|---|---|---|
| 1 | Wave-Mamba: Wavelet State Space Model for Ultra-High-Definition Low-Light Image Enhancement IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: It enables state space models (SSMs) to avoid being affected by noise when modeling long sequences, thus making full use of the long-sequence modeling capability of SSMs. On this basis, we propose Wave-Mamba, a novel approach based on two pivotal insights derived from the wavelet domain: 1) most of the content information of an image exists in the low-frequency component, less in the high-frequency component. | Wenbin Zou; Hongxia Gao; Weipeng Yang et al. | mm | 2024-10-30 |
| 2 | Tango 2: Aligning Diffusion-based Text-to-Audio Generations Through Direct Preference Optimization IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: As such, in this work, using an existing text-to-audio model Tango, we synthetically create a preference dataset where each prompt has a winner audio output and some loser audio outputs for the diffusion model to learn from. | Navonil Majumder; Chia-Yu Hung; Deepanway Ghosal et al. | mm | 2024-10-30 |
| 3 | DiffMM: Multi-Modal Diffusion Model for Recommendation IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: To fill this research gap, we propose a novel multi-modal graph diffusion model for recommendation called DiffMM. | Yangqin Jiang; Lianghao Xia; Wei Wei et al. | mm | 2024-10-30 |
| 4 | FreqMamba: Viewing Mamba from A Frequency Perspective for Image Deraining IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: In this paper, we propose FreqMamba, an effective and efficient paradigm that leverages the complementary between Mamba and frequency analysis for image deraining. | Zhen Zou; Hu Yu; Jie Huang et al. | mm | 2024-10-30 |
| 5 | Making Large Language Models Perform Better in Knowledge Graph Completion IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Save Highlight: In this paper, we explore methods to incorporate structural information into the LLMs, with the overarching goal of facilitating structure-aware reasoning. | Yichi Zhang; Zhuo Chen; Lingbing Guo et al. | mm | 2024-10-30 |
This page lists the papers of MM 2024, ordered by impact or potential impact.