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cs.CV2025
GridPrune: From "Where to Look" to "What to Select" in Visual Token Pruning for MLLMs
Yuxiang Duan, Ao Li, Yingqin Li +2
Multimodal large language models (MLLMs) have shown remarkable capabilities in a wide range of vision-language tasks. However, the large number of visual tokens introduces signific…
cs.CV2025
TransPrune: Token Transition Pruning for Efficient Large Vision-Language Model
Ao Li, Yuxiang Duan, Jinghui Zhang +5
Large Vision-Language Models (LVLMs) have advanced multimodal learning but face high computational costs due to the large number of visual tokens, motivating token pruning to impro…
cs.CV2025
Modeling Variants of Prompts for Vision-Language Models
Ao Li, Zongfang Liu, Xinhua Li +3
Large pre-trained vision-language models (VLMs) offer a promising approach to leveraging human language for enhancing downstream tasks. However, VLMs such as CLIP face significant…