Efficient LLaMA-3.2-Vision by Trimming Cross-attended Visual Features

• URL: http://arxiv.org/abs/2504.00557v1
• Date: Tue, 01 Apr 2025 09:10:32 GMT
• Title: Efficient LLaMA-3.2-Vision by Trimming Cross-attended Visual Features
• Authors: Jewon Lee, Ki-Ung Song, Seungmin Yang, Donguk Lim, Jaeyeon Kim, Wooksu Shin, Bo-Kyeong Kim, Yong Jae Lee, Tae-Ho Kim,
• Abstract summary: We exploit the sparse nature in cross-attention maps to selectively prune redundant visual features.<n>Our model can reduce inference latency and memory usage while achieving benchmark parity.
• Score: 24.33252753245426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual token reduction lowers inference costs caused by extensive image features in large vision-language models (LVLMs). Unlike relevant studies that prune tokens in self-attention-only LVLMs, our work uniquely addresses cross-attention-based models, which achieve superior performance. We identify that the key-value (KV) cache size for image tokens in cross-attention layers significantly exceeds that of text tokens in self-attention layers, posing a major compute bottleneck. To mitigate this issue, we exploit the sparse nature in cross-attention maps to selectively prune redundant visual features. Our Trimmed Llama effectively reduces KV cache demands without requiring additional training. By benefiting from 50%-reduced visual features, our model can reduce inference latency and memory usage while achieving benchmark parity.

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