1 citations · 3 across the 24 of their papers we have counts for
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Sharp Eyes and Memory for VideoLLMs: Information-Aware Visual Token Pruning for Efficient and Reliable VideoLLM Reasoning
Jialong Qin, Xin Zou, Di Lu +2
Current Video Large Language Models (VideoLLMs) suffer from quadratic computational complexity and key-value cache scaling, due to their reliance on processing excessive redundant…
Don't Just Chase "Highlighted Tokens" in MLLMs: Revisiting Visual Holistic Context Retention
Xin Zou, Di Lu, Yizhou Wang +5
Despite their powerful capabilities, Multimodal Large Language Models (MLLMs) suffer from considerable computational overhead due to their reliance on massive visual tokens. Recent…
Are We Using the Right Benchmark: An Evaluation Framework for Visual Token Compression Methods
Chenfei Liao, Wensong Wang, Zichen Wen +10
Recent efforts to accelerate inference in Multimodal Large Language Models (MLLMs) have largely focused on visual token compression. The effectiveness of these methods is commonly…
DocPruner: A Storage-Efficient Framework for Multi-Vector Visual Document Retrieval via Adaptive Patch-Level Embedding Pruning
Yibo Yan, Guangwei Xu, Xin Zou +3
Visual Document Retrieval (VDR), the task of retrieving visually-rich document pages using queries that combine visual and textual cues, is crucial for numerous real-world applicat…
GM-PRM: A Generative Multimodal Process Reward Model for Multimodal Mathematical Reasoning
Jianghangfan Zhang, Yibo Yan, Kening Zheng +3
Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities but often struggle with complex, multi-step mathematical reasoning, where minor errors in visual percep…
Pierce the Mists, Greet the Sky: Decipher Knowledge Overshadowing via Knowledge Circuit Analysis
Haoming Huang, Yibo Yan, Jiahao Huo +4
Large Language Models (LLMs), despite their remarkable capabilities, are hampered by hallucinations. A particularly challenging variant, knowledge overshadowing, occurs when one pi…