AnchorPrune: Relevance-Anchored Contextual Expansion for Visual Token Pruning
- Published
- Source
- arXiv
- Paper number
- 586
- Field
- Computer Vision
- arXiv ID
- 2607.07033
Key points
- Its sequential design first anchors relevance and then expands context, which prioritizes non-redundant query evidence.
- It automatically chooses the anchor size from each token's novelty profile rather than using a fixed ratio.
- The architecture-aware design applies to both CLIP-aligned and unaligned models, as well as image and video VLMs.
- On LLaVA-NeXT-7B, it retains 97.6 percent of full performance with only 160 of 2,880 tokens, or 5.6 percent.
- At extreme compression with 32 tokens, it outperforms prior relevance-diversity integrated methods by 3.7 to 5.7 percentage points.
- It is a lightweight framework that requires no training and can be applied directly to existing VLMs without architectural changes.
Paper links
External research summaries. These are not HDATF publications or measured product results.