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Deep transfer learning aims at dealing with challenges in new tasks with insufficient samples. However, when it comes to few-shot learning scenarios, due to the low diversity of several known training samples, they are prone to be dominated by specificity, thus leading to one-sidedness local features instead of the reliable global feature of the actual categories they belong to. To alleviate the difficulty, we propose a cross-modal few-shot contextual transfer method that leverages thedoi:10.3389/fnbot.2021.654519 pmid:34108871 pmcid:PMC8180855 fatcat:ue5u75pc6nf7hgpkbj6zjry4ie