Interpretable Cross-Domain Few-Shot Learning with Rectified Target-Domain Local Alignment

Cross-Domain Few-Shot Learning (CDFSL) adapts models trained with large-scale general data (source domain) to downstream target domains with only scarce training data, where the research on vision-language models (e.g., CLIP) is still in the early stages. Typical downstream domains, such as medical diagnosis, require fine-grained visual cues for interpretable recognition, but we find that current fine-tuned CLIP models can hardly focus on these cues, albeit they can roughly focus on important re

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ethics.ai (18 March 2026), “Interpretable Cross-Domain Few-Shot Learning with Rectified Target-Domain Local Alignment,” evidence record 7068, https://ethics.ai/record/7068 (originally published by arXiv).

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