ERASE: Eliminating Redundant Visual Tokens via Adaptive Two-Stage Token Pruning
Recent advances in Vision-Language Models (VLMs) enable large language models (LLMs) to process high-resolution images, significantly improving real-world multimodal understanding. However, this capability introduces a large number of vision tokens, incurring substantial computational overhead. To mitigate this issue, various vision token compression methods have been proposed. Existing methods often estimate different sources of visual redundancy using learned representations or fixed pruning schedules. We propose ERASE, an adaptive two-stage framework that separates image-level redundancy removal from instruction-dependent token pruning. Stage 1 derives image-dependent token retention f...
| Authors | Yuna Lee, Kyoungho Min, Yulhwa Kim |
|---|---|
| Primary category | cs.CV |
| Published | 11 May 2026 |
| Last updated at source | 8 October 2026 |
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