Spatial self-attention network with self-attention distillation for fine-grained image recognition

The underlining task for fine-grained image recognition captures both the inter-class and intra-class discriminate features. Existing methods generally use auxiliary data to guide the network or a complex network comprising multiple sub-networks. They have two significant drawbacks: (1) Using auxiliary data like bounding boxes requires expert knowledge and expensive data annotation. (2) Using multiple sub-networks make network architecture complex and requires complicated training or multiple training steps. We propose an end-to-end Spatial Self-Attention Network (SSANet) comprising a spatial self-attention module (SSA) and a self-attention distillation (Self-AD) technique. The SSA encodes contextual information into local features, improving intra-class representation. Then, the Self-AD distills knowledge from the SSA to a primary feature map, obtaining inter-class representation. By accumulating classification losses from these two modules enables the network to learn both inter-class and intra-class features in one training step. The experiment findings demonstrate that SSANet is effective and achieves competitive performance.