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arXiv · 2404.08567

CATP: Cross-Attention Token Pruning for Accuracy Preserved Multimodal Model Inference

Ruqi Liao·Chuqing Zhao·Jin Li·Weiqi Feng·Yi Lyu·Bingxian Chen·Haochen Yang
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Abstract

In response to the rising interest in large multimodal models, we introduce Cross-Attention Token Pruning (CATP), a precision-focused token pruning method. Our approach leverages cross-attention layers in multimodal models, exemplified by BLIP-2, to extract valuable information for token importance determination. CATP employs a refined voting strategy across model heads and layers. In evaluations, CATP achieves up to 12.1X higher accuracy compared to existing token pruning methods, addressing the trade-off between computational efficiency and model precision.

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Ruqi Liao, Chuqing Zhao, Jin Li, Weiqi Feng, Yi Lyu, Bingxian Chen, Haochen Yang. 2024-04-02. CATP: Cross-Attention Token Pruning for Accuracy Preserved Multimodal Model Inference. https://arxiv.org/abs/2404.08567

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