AIBullisharXiv – CS AI · Mar 96/10
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VLMQ: Token Saliency-Driven Post-Training Quantization for Vision-language Models
Researchers introduced VLMQ, a post-training quantization framework specifically designed for vision-language models that addresses visual over-representation and modality gaps. The method achieves significant performance improvements, including 16.45% better results on MME-RealWorld under 2-bit quantization compared to existing approaches.