Multi-branch selection fusion fine-grained classification algorithm based on coordinate attention localization

Object localization has been the focus of research in FGVC(Fine-Grained Visual Categorization). With the aim of improving the accuracy and precision of object localization in multi-branch networks, as well as the robustness and universality of object localization methods, our study mainly focus on how to combines coordinate attention and feature activation map for target localization. The model in this paper is a three-branch model including raw branch, object branch and part branch. The images are fed directly into the raw branch. CAOLM is used to localize and crop objects in the image to generate the input for the object branch. APPM is used to propose part regions at different scales. The three classes of input images undergo end-to-end weakly supervised learning through different branches of the network. The model expands the receptive field to capture multi-scale features by SB-ASPP. It can fuse the feature maps obtained from the raw branch and the object branch with SBBlock, and the complete features of the raw branch are used to supplement the missing information of the object branch. Extensive experimental results on CUB-200-2011, FGVC-Aircraft and Stanford Cars datasets show that our method has the best classification performance on FGVC-Aircraft and also has competitive performance on other datasets. Few parameters and fast inference speed are also the advantages of our model.