QUOTA jointly optimizes low-bit quantization and visual token pruning for VLMs by deriving pruning decisions from quantized operators, achieving 95.65% average performance retention with only 30% of visual tokens versus 94.3% for stage-wise baselines.
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Towards Joint Quantization and Token Pruning of Vision-Language Models
QUOTA jointly optimizes low-bit quantization and visual token pruning for VLMs by deriving pruning decisions from quantized operators, achieving 95.65% average performance retention with only 30% of visual tokens versus 94.3% for stage-wise baselines.