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cs.CV2026
VPRune: Efficient Training-free Pre-LLM Visual Token Pruning
Guangchuan Lv, Dianxing Shi, Dingjie Fu
Visual token pruning is a promising approach to reducing the inference cost of large vision-language models (LVLMs), yet aggressive token reduction often causes substantial perform…
cs.CV2026
Reconstructing Is Not Acting: Action-Centric Latent Dynamics Modeling
Dingjie Fu, Dianxing Shi, Yangyang Xu +1
Latent action models (LAMs) learn action representations from unlabeled videos by inferring latent actions from visual transitions and reconstructing future states. However, we ide…
cs.CV2026
Can Synthetic Images Serve as Effective and Efficient Class Prototypes?
Dianxing Shi, Dingjie Fu, Yuqiao Liu +1
Vision-Language Models (VLMs) have shown strong performance in zero-shot image classification tasks. However, existing methods, including Contrastive Language-Image Pre-training (C…