Cross-attention based dual-similarity network for few-shot learning

Few-shot classification is a challenging task to recognize unseen classes with limited data. Following the success of Vision Transformer in various large-scale datasets image recognition domains, recent few-shot classification methods employ transformer-style. However, most of them focus only on cross-attention between support and query sets, mainly considering channel-similarity. To address this issue, we introduce dual-similarity network (DSN) in which attention maps for the same target within a class are made identical. With the network, a way of effective training through the integration of the channel-similarity and the map-similarity has been sought. Our method, while focused on $N$-way $K$-shot scenarios, also demonstrates strong performance in 1-shot settings through augmentation. The experimental results verify the effectiveness of DSN on widely used benchmark datasets.