MoPrune: Scene-Guided Motion-Aware Token Pruning for Efficient Video Large Language Models (2026.findings-acl)
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| Challenge: | Prior approaches to token pruning ignore video dynamics and the fact that different scenes exhibit different redundancy patterns. |
| Approach: | They propose a token pruning framework that is train-free and scene-guided to accelerate VideoLLMs by removing redundant visual information from video frames. |
| Outcome: | MoPrune is a training-free, scene-guided and motion-centric token pruning framework for accelerating VideoLLMs. |
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| Challenge: | Existing methods to prune redundant vision tokens struggle in shallow layers due to the lack of contextual information. |
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| Challenge: | Large Vision-Language Models (LVLMs) excel at visual understanding but face severe computational bottlenecks when processing high-resolution images and long videos due to massive visual token counts. |
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| Challenge: | Existing methods for visual token pruning compromise the integrity of visual understanding in pursuit of efficiency. |
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| Challenge: | Recent studies have shown that Video Large Language Models (Vide-oLLMs) are efficient at video understanding but lack the quadratic complexity of visual tokens. |
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