A target-driven active learning approach for building efficient prompt sets in microscopy VLMs reaches 100% test accuracy with an average of 20 expert-verified images, outperforming random selection.
Minilmv2: Multi-head self-attention relation dis- tillation for compressing pretrained transformers
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A Human-in-the-Loop Framework for Efficient Prompt Selection in Microscopy Vision-Language Models
A target-driven active learning approach for building efficient prompt sets in microscopy VLMs reaches 100% test accuracy with an average of 20 expert-verified images, outperforming random selection.