arXiv Computer Vision
Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Pruning
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Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Prunin
arXiv:2608. 06411v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) achieve strong performance across diverse vision-language tasks, but their efficiency is limited by the cost of processing numerous visual tokens.
arXiv Computer Vision
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LearnPruner: Rethinking Attention-based Token Pruning in Vision Language Models
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Not All Attention Heads Contribute to Critical Visual Token Selection: Head-Aware Pruning Matters More
The paper shows that only a small subset of attention heads in vision-language models is responsible for selecting critical visual tokens. By pruning tokens based on similarity before LLM reasoning and then applying head‑aware pruning during reasoning, the proposed ProViP framework achieves high task performance with significant speedups. Experiments on LLaVA‑1.5‑7B demonstrate 95.9% performance retention and a 1.62× inference speedup at an 88.9% pruning ratio.
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DIPrune: Task-Aware Token Pruning with Dual Importance for Efficient Multimodal Language Models
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TReVS: Integrating Textual Relevance and Visual Saliency for Efficient Vision-Language Model Token Pruning
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arXiv Computer Vision
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Representation Dynamics Reveal Semantic Saliency and Similarity for Visual Token Pruning in MLLMs
arXiv:2609.36916v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) incur high inference latency from long visual token sequences. Existing pruning methods commonly use attention...