Author

Xinghao Chen

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Preprint Aug 2026

SlimVLM: Sensitivity-aware Dynamic Structured Pruning with Adaptive Visual Token Selection for Efficient Vision-Language Models

This work introduces an adaptive visual token selection strategy for VLMs that leverages average text-to-visual attention scores to assess the importance of visual tokens, removing redundant ones during pruning based on a set threshold, thereby optimizing the importance calculation.

Yaozhi Wen, Jialong Guo, Zhenliang Ni et al. · 0 citations