1 citations · 2 across the 14 of their papers we have counts for
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ERA: Entropy-Guided Visual Token Pruning with Rectified Attention for Efficient MLLMs
Yuhao Wang, Mu Qiao, Haiwen Diao +5
Multimodal Large Language Models (MLLMs) incur prohibitive inference costs due to long visual token sequences. Training-free visual token reduction provides an efficient solution.…
MotionAtlas: Detailed Region Captioning for Motion-Centric Videos
Weisong Liu, Haochen Wang, Kuan Gao +8
We propose MotionAtlas, a system for detailed captioning of motion-centric videos, comprising (1) a dedicated human-annotated benchmark, (2) a scalable, high-quality pipeline to co…
PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models
Yueyi Sun, Yuhao Wang, Jason Li +8
Multimodal large language models (MLLMs) have achieved remarkable progress in visual understanding tasks. However, most existing MLLMs rely on autoregressive generation, which limi…
VISTA-Bench: Do Vision-Language Models Really Understand Visualized Text as Well as Pure Text?
Qing'an Liu, Juntong Feng, Yuhao Wang +6
Vision-Language Models (VLMs) have achieved impressive performance in cross-modal understanding across textual and visual inputs, yet existing benchmarks predominantly focus on pur…
W2S-AlignTree: Weak-to-Strong Inference-Time Alignment for Large Language Models via Monte Carlo Tree Search
Zhenyu Ding, Yuhao Wang, Tengyue Xiao +3
Large Language Models (LLMs) demonstrate impressive capabilities, yet their outputs often suffer from misalignment with human preferences due to the inadequacy of weak supervision…