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Unsupervised Transfer Clustering for Mitigating Cold Start in Active Prompt Learning

arXiv · AI, language, vision and robotics · article · Sep 4, 2026 · UTC

Vision-Language Models (VLMs) are able to achieve impressive zero-shot classification performance by aligning visual and textual representations, but each new task still demands handcrafted prompts. Active Prompt Learning (APL) combines Active Learning (AL) and Prompt Learning (PL) into a single framework, allowing for the usage of the VLM prior knowledge for iteratively querying the most informative images to be labeled. However, the cold-start problem is still relevant for APL methods, where the performance of the initial query can be worse than random sampling. While recent state-of-the-art

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