arXiv Computer Vision
Just Noticeable Difference Modeling for Token Compression in Vision-Language-Action Models
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SA-VLA: State-aware tokenizer for improving Vision-Language-Action Models' performance
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LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models
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Role-Conditioned Sub-Token Routing for Efficient Vision-Language-Action Policies
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Hugging Face Trending Papers
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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.
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