Eun-Ju Park

CatchUp: Depth-Aware Re-Admission of Visual Tokens for Efficient Large Vision-Language Models

Large Vision-Language Models (LVLMs) repeatedly process hundreds of visual tokens across decoder layers, incurring substantial computation and memory costs. Existing visual-token reduction methods often irreversibly discard tokens despite depth-varying query relevance. Although recoverable routing allows excluded token...