PruneVid: Visual Token Pruning for Efficient Video Large Language Models (2025.findings-acl)
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| Challenge: | Existing approaches to video token pruning face significant computational challenges due to the redundancy inherent in video data. |
| Approach: | They propose a training-free visual token pruning method that reduces the redundancy inherent in video data and leverages LLMs’ inherent ability to selectively prune visual tokens irrelevant to specific queries. |
| Outcome: | The proposed method can prune over 80% of tokens while maintaining competitive performance when combined with different video LLMs. |
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| Challenge: | Existing visual token compression methods rely on attention scores but have inherent biases . global and local attention biased scores cause excessive computational overhead . |
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| Challenge: | Multimodal large language models have shown remarkable performance for cross-modal understanding and generation, yet suffer from severe inference costs. |
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