How does the accuracy of few-shot adapted medical VLMs correlate with the number of adaptation examples provid

Abstract

Abstract: Current pre-trained vision-language models (PVLMs) achieve excellent performance on a range of multi-modal datasets.Recent work aims at building multilingual versions of such models, and a range of multilingual multimodal datasets have been introduced for this purpose.However, current PVLMs typically perform poorly on such datasets when used for zero-shot or few-shot cross-lingual transfer, especially for low-resource languages.To alleviate this problem, we propose a novel meta-learning fine-tuning framework.Our framework makes it possible to rapidly adapt PVLMs to new languages by using Model

Research Question

How does the accuracy of few-shot adapted medical VLMs correlate with the number of adaptation examples provided on NLVR2 and SNLI-VE benchmarks

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