C-PEAL trains the active learning selector with a loss that raises entropy for wrong predictions and lowers it for correct ones, improving sample selection for CLIP-style models under prompt learning and LoRA.
Deepseek-vl: Towards real-world vision-language understanding,
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Optimizing Active Learning in Vision-Language Models via Parameter-Efficient Uncertainty Calibration
C-PEAL trains the active learning selector with a loss that raises entropy for wrong predictions and lowers it for correct ones, improving sample selection for CLIP-style models under prompt learning and LoRA.