TReVS: Integrating Textual Relevance and Visual Saliency for Efficient Vision-Language Model Token Pruning

Vision-Language Models (VLMs) excel at visual understanding and reasoning but often incur substantial inference costs due to the large number of visual tokens. Recent visual token pruning methods increasingly follow a two-stage paradigm: they first remove visually redundant tokens after the vision e