Exploiting category similarity-based distributed labeling for fine-grained visual classification

Abstract

The fine-grained visual classification (FGVC) which aims to distinguish subtle differences among subcategories is an important computer vision task. However, one issue that limits model performance is the problem of diversity within subcategories. To this end, we propose a simple yet effective approach named category similarity-based distributed labeling (CSDL) to tackle this problem. Specifically, we first obtain the feature centers for various subcategories and utilize them to initialize the label distributions. Then we replace the ground-truth labels in a Deep Neural Network (DNN) with the distributed labels to calculate the loss and perform the optimization. Finally, the joint supervision of a softmax loss and a center loss is adopted to update the parameters of the DNN, the deep feature centers, and the distributed labels for learning discriminative deep features. Comprehensive experiments on three publicly available FGVC datasets demonstrate the superiority of our proposed approach.