Multimodal cross-decoupling for few-shot learning

Current multi-modal few-shot learning methods overlook the impact of inter-attribute differences on accurately recognizing sample categories. To address this problem, a multimodal cross-decoupling method was proposed which could decouple semantic features with different attributes and reconstruct the essential category features of samples, aiming to alleviate the impact of category attribute differences on category discrimination. Extensive experiments on two benchmark few-shot datasets MIT-States and C-GQA with large attribute discrepancy indicates that the proposed method outperforms the existing approaches, which fully verifies its effectiveness, indicating that the multimodal cross-decoupling few-shot learning method can improve the classification performance of identifying few test samples.