Coupled Patch Similarity Network For One-Shot Fine-Grained Image Recognition

One-shot fine-grained image recognition (OSFG) aims to distinguish different fine-grained categories with only one training sample per category. Previous works mainly focus on learning a global feature representation through only a using single similarity metric branch, which is unsuitable for OSFG to effectively capture subtle and local differences under limited supervision. In this work, we propose a Coupled Patch Similarity Network (CPSN) for OSFG. Firstly, we propose a Feature Enhancement Module (FEM) to extract more discriminative features of the fine-grained samples. Then, we develop two coupled and symmetrical branches to capture discriminative parts of the samples and reduce the deviation of the distance metric. For each branch, we design a Patch Similarity Module (PSM) to calculate the patch similarity map for the sample pair. Especially, a Patch Weight Generator (PWG) is proposed to generate the patch weight map, which indicates the degree of importance for each position in the patch similarity map, so that the model can focus on diverse and informative parts. We analyze the effect of the different components in the proposed network, and extensive experimental results demonstrate the effectiveness and superiority of the proposed method on two fine-grained benchmark datasets.