Abstract Multimodal few-shot learning aims to exploit complementary information inherent in multiple modalities for vision tasks in low data scenarios. Most of the current research focuses on a suitable embedding space for the various modalities. While solutions based on embedding provide state-of-the-art results. they reduce the interpretability of the model. https://ashleyshomestores.shop/product-category/swivel-glider-recliner/
Multimodal few-shot classification without attribute embedding
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