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MAG leverages abundant unlabeled multi-modal data to boost few-shot learning performance, achieving significant gains in label-scarce environments.
Achieving state-of-the-art multimodal performance with only 208.62 million unique images and a theoretical training cost of just $400K challenges the notion that larger datasets and budgets are always necessary for success.
