DeSAP uses decoupled cross-modal similarity plus visual saliency to prune visual tokens in LVLMs, retaining 11.1% tokens for 10x FLOPs reduction and 98.1% performance on LLaVA-1.5-7B.
As shown in Table 7, our method consistently maintains competitive performance across multiple video under- standing benchmarks while retaining only 50% of the visual token budget
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Decoupled Similarity for Task-Aware Token Pruning in Large Vision-Language Models
DeSAP uses decoupled cross-modal similarity plus visual saliency to prune visual tokens in LVLMs, retaining 11.1% tokens for 10x FLOPs reduction and 98.1% performance on LLaVA-1.5-7B.