Guang-Chuan Lv

VPRune: Efficient Training-free Pre-LLM Visual Token Pruning

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 performance degradation. We identify three key factors behind this degradation: text-guided selection bias, information loss from discarded tokens,...