Combating Textual Noise and Redundancy: Entropy-Aware Dense Visual Token Pruning
Visual token pruning is a crucial strategy for accelerating VLMs by compressing redundant image patches, yet existing methods often fail to preserve critical cues under dense instructions and fine-grained queries. In this paper, we investigate this failure and identify two underlying bottlenecks: the widespread dispersion of textual noise that corrupts dense cross-modal scoring, and the feature fragmentation inherent to standard token selection. To address these issues, we propose Entropy-Aware Dense Pruning (EADP), a framework that reformulates pruning as a structured compression problem. EAD
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- LinkedLinked via arxiv author · 85%Xuehui Wang →
“Combating Textual Noise and Redundancy: Entropy-Aware Dense Visual Token Pruning”
- LinkedLinked via arxiv author · 85%Xuankun Yang →
“Combating Textual Noise and Redundancy: Entropy-Aware Dense Visual Token Pruning”
- LinkedLinked via arxiv author · 85%Juwei Shen →
“Combating Textual Noise and Redundancy: Entropy-Aware Dense Visual Token Pruning”