AnchorPrune: Relevance-Anchored Contextual Expansion for Visual Token Pruning
Large vision-language models incur substantial inference costs because high-resolution inputs introduce thousands of visual tokens, many of which are redundant for a given query. Existing pruning methods often combine query relevance and token diversity, yet these objectives can conflict under aggressive compression: relevance-driven selection may overconcentrate the budget on correlated local evidence, while diversity-driven selection may suppress indispensable tokens or retain distinct but uninformative regions. We introduce AnchorPrune, a training-free framework that first constructs a protected relevance anchor and then expands it with complementary visual context. AnchorPrune adaptiv...
| Authors | Kyuan Oh, Bumsoo Kim |
|---|---|
| Primary category | cs.CV |
| Published | 8 July 2026 |
| Last updated at source | 10 September 2026 |
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