Adaptive Visual Token Reduction for Accelerated Image Understanding
Large Vision-Language Models achieve strong VQA performance, but processing high-resolution, information-rich images requires substantial computation, motivating visual token reduction. However, existing methods often prune individual tokens or rely on fixed-size cropping, limiting their ability to preserve spatially structured information such as horizontally or vertically elongated text. To address this limitation, we propose ReFIT, an instruction-guided visual token reduction framework for efficient LVLM inference. ReFIT consists of Relevance-Guided Window Reshaping (RWR) and Instruction-Guided Token Refinement (ITR), where RWR captures instruction-relevant regions by adapting to their...
| Authors | Seyoung Jeong, Jong Pil Yun, Sang Jun Lee |
|---|---|
| Primary category | cs.CV |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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