1 citations · 1 across the 5 of their papers we have counts for
5 papers
Exposure Bias Can Alleviate Itself via Directional and Frequency Rectification in Flow Matching
Guanbo Huang, Jingjia Mao, Fanding Huang +9
Flow Matching (FM) has achieved remarkable generative performance, yet it suffers from exposure bias due to discrepancies between training and inference. Existing mitigation strate…
ReflexFlow: Rethinking Learning Objective for Exposure Bias Alleviation in Flow Matching
Guanbo Huang, Jingjia Mao, Fanding Huang +8
Despite tremendous recent progress, Flow Matching methods still suffer from exposure bias due to discrepancies in training and inference. This paper investigates the root causes of…
MAJORScore: A Novel Metric for Evaluating Multimodal Relevance via Joint Representation
Zhicheng Du, Qingyang Shi, Jiasheng Lu +4
The multimodal relevance metric is usually borrowed from the embedding ability of pretrained contrastive learning models for bimodal data, which is used to evaluate the correlation…
Hear-Your-Click: Interactive Object-Specific Video-to-Audio Generation
Yingshan Liang, Keyu Fan, Zhicheng Du +5
Video-to-audio (V2A) generation shows great potential in fields such as film production. Despite significant advances, current V2A methods relying on global video information strug…
BATON: Aligning Text-to-Audio Model with Human Preference Feedback
Huan Liao, Haonan Han, Kai Yang +7
With the development of AI-Generated Content (AIGC), text-to-audio models are gaining widespread attention. However, it is challenging for these models to generate audio aligned wi…