VPRune: Efficient Training-free Pre-LLM Visual Token Pruning
Experiments on FastVLM-1.5B across multiple vision-language benchmarks demonstrate that VPRune achieves a favorable accuracy--compression trade-off, with particularly pronounced advantages under aggressive compression, demonstrating its practicality for resource-constrained LVLM deployment.
Guang-Chuan Lv, Dian-Xing Shi, Ding-Jie Fu
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