arXiv Computer Vision
VPRune: Efficient Training-free Pre-LLM Visual Token Pruning
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VPRune: Efficient Training-free Pre-LLM Visual Token Pruning
Visual token pruning is a promising approach to reducing the inference cost of large vision-language models (LVLMs), yet aggressive token reduction often causes substantial performance degradation. We...
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Multi-Image Visual Token Pruning in Large Visual Language Models
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Beyond One-Size-Fits-All: Sample-Adaptive Strategy Routing for Vision Token Pruning in MLLMs
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When Text Matters: Design Principles for Visual Token Pruning in Vision-Language Model
The paper introduces a training‑free visual token pruning strategy for vision‑language models that separates early vision‑guided pruning from later text‑guided reselection. By first pruning tokens with vision‑encoder attention, retaining candidates until the decoder midpoint, and then applying text‑to‑visual attention, the method preserves task‑relevant visual information. Across eight benchmarks and three models, it achieves an average performance recovery of 11.10 and 16.84 percentage points at 80% and 90% pruning, respectively, while maintaining comparable or lower LLM‑prefill latency.
arXiv Computer Vision
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Not All Attention Heads Contribute to Critical Visual Token Selection: Head-Aware Pruning Matters More
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STAR-Pro: Stage-Wise Token Adaptive Reduction with Progressive Refinement for Efficient Large Vision-Language Models
arXiv:2609.05916v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) achieve strong multimodal understanding, but the hundreds to thousands of visual tokens they process impose subs...