Beyond Attention Scores: SVD-Based Vision Token Pruning for Efficient Vision-Language Models

arXiv:2604.11530v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have revolutionized multi-modal learning by jointly processing visual and textual information. Yet, they face significant challenges due to the high computational and memory demands of processing long sequences of vision tokens. Many existing methods rely on local heuristics, such as attention scores or token norms. However, these criteria suffer from positional bias and information dispersion, limiting their ability to preserve essential content at high pruning ratios and leading to performance degradation

Source: arXiv cs.AI — read the full report at the original publisher.

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