Contrastive Learning with Part Assignment for Fine-grained Ship Image Recognition

Abstract

Fine-grained ship image recognition is to discriminate different subcategories of ship categories. Because of the lack of ship data sets and the particularity of the identification task, fine-grained ship recognition is a challenging task. We designed a part assignment module, which has the function of part assignment and extracting import part information. Then, we added the module to the SimCLR contrastive learning framework. This method uses the module to assignment the information in the feature map, extract the key information of key regions, increase the learning ability of contrast learning for key information, in the end, the accuracy of fine-grained classification can be improved.