arXiv AI
When 1+1 Backfires: Rethinking Relevance-Diversity Balancing for Sample-Aware Visual Token Reduction
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AnchorPrune: Relevance-Anchored Contextual Expansion for Visual Token Pruning
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GOTS: Greedy Orthogonal Token Selection for High-Resolution Vision-Language Models
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TReVS: Integrating Textual Relevance and Visual Saliency for Efficient Vision-Language Model Token Pruning
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StackTok: Accelerating VLMs Inference with Budget-Adaptive Visual Token Selection
arXiv:2609.16841v1 Announce Type: cross Abstract: Increasing image resolution produces ever-longer visual-token sequences in vision-language models (VLMs), substantially raising their inference cost....
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TOPS: First-Principles Visual Token Pruning via Constructing Token Optimal Preservation Sets for Efficient MLLM Inference
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Hugging Face Trending Papers
Aug 11
When Vision Becomes Text: Visual Token Pruning via Cross-Modal Residual Guidance in VLMs
Abundant visual information strengthens vision-language model (VLM) perception, yet massive visual tokens raise inference costs. Existing visual token pruning methods rely on similarity-based guidance, which exploits pairwise text-vision and vision-vision token correlations for compression.