2 citations · 2 across the 8 of their papers we have counts for
5 papers · 1 filter
EM-KD: Distilling Efficient Multimodal Large Language Model with Unbalanced Vision Tokens
Ze Feng, Sen Yang, Boqiang Duan +2
Efficient Multimodal Large Language Models (MLLMs) compress vision tokens to reduce resource consumption, but the loss of visual information can degrade comprehension capabilities.…
MomentSeg: Moment-Centric Sampling for Enhanced Video Pixel Understanding
Ming Dai, Sen Yang, Boqiang Duan +2
Referring Video Object Segmentation (RefVOS) seeks to segment target objects in videos guided by natural language descriptions, demanding both temporal reasoning and fine-grained v…
Improving Generalized Visual Grounding with Instance-aware Joint Learning
Ming Dai, Wenxuan Cheng, Jiang-Jiang Liu +4
Generalized visual grounding tasks, including Generalized Referring Expression Comprehension (GREC) and Segmentation (GRES), extend the classical visual grounding paradigm by accom…
PropVG: End-to-End Proposal-Driven Visual Grounding with Multi-Granularity Discrimination
Ming Dai, Wenxuan Cheng, Jiedong Zhuang +4
Recent advances in visual grounding have largely shifted away from traditional proposal-based two-stage frameworks due to their inefficiency and high computational complexity, favo…
Vision Remember: Recovering Visual Information in Efficient LVLM with Vision Feature Resampling
Ze Feng, Jiang-jiang Liu, Sen Yang +5
The computational expense of redundant vision tokens in Large Vision-Language Models (LVLMs) has led many existing methods to compress them via a vision projector. However, this co…