3 papers
cs.CV2025
Grounded Chain-of-Thought for Multimodal Large Language Models
Qiong Wu, Xiangcong Yang, Yiyi Zhou +4
Despite great progress, existing multimodal large language models (MLLMs) are prone to visual hallucination, greatly impeding their trustworthy applications. In this paper, we stud…
cs.CV2025
What Kind of Visual Tokens Do We Need? Training-free Visual Token Pruning for Multi-modal Large Language Models from the Perspective of Graph
Yutao Jiang, Qiong Wu, Wenhao Lin +2
Recent Multimodal Large Language Models(MLLMs) often use a large number of visual tokens to compensate their visual shortcoming, leading to excessive computation and obvious visual…
cs.CV2024
Accelerating Multimodal Large Language Models via Dynamic Visual-Token Exit and the Empirical Findings
Qiong Wu, Wenhao Lin, Yiyi Zhou +4
The excessive use of visual tokens in existing Multimoal Large Language Models (MLLMs) often exhibits obvious redundancy and brings in prohibitively expensive computation. To gain…