多模态交叉解耦的少样本学习方法

当前的多模态少样本学习方法忽视了属性间差异对正确识别样本类别的影响。针对这一问题，提出一种利用多模态交叉解耦的方法，通过解耦不同属性语义特征，并经过特征重建学习样本的本质类别特征，缓解类别属性差异对类别判别的影响。在两个属性差异较大的基准少样本数据集MIT-States和C-GQA上进行的大量实验表明，所提方法较现有方法有较大的性能提升，充分验证了方法的有效性，表明多模态交叉解耦的少样本学习方法能够提升识别少量测试样本的分类性能。

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.