CROSS-MODALITY GRAPH NEURAL NETWORK FOR FEW-SHOT LEARNING

Few-shot learning, which attempts to predict unlabeled samples with only a few labeled samples, has drawn more and more attention. Though recent works have achieved promising progress, none of them have noticed to establish consistency among episodes, leading to the ambiguity in latent embedding space. In this paper, we propose a novel Cross-Modality Graph Neural Network (CMGNN) to uncover the associations among episodes for consistent global embedding. Since the semantic information induced from NLP is relatively fixed compared to visual information space, we leverage it to construct meta nodes for each category to guide the corresponding visual feature learning through GNN. Moreover, to ensure global embedding, a distance loss function is designed to force the visual nodes closer to their associated meta nodes to a greater extent. Extensive experiments and ablation studies on four benchmark datasets show its superiority over many SOTA comparison methods.