Prototype-based Classifier Learning for Long-Tailed Visual Recognition.

In this paper,we tackle the long-tailed visual recognition problem from the categorical prototype perspective by proposing a prototype-based classifier learning(PCL)method.Specifically,thanks to the gen-eralization ability and robustness,categorical prototypes reveal their advantages of representing the category semantics.Coupled with their class-balance characteristic,categorical prototypes also show potential for han-dling data imbalance.In our PCL,we propose to generate the categorical classifiers based on the prototypes by performing a learnable mapping function.To further alleviate the impact of imbalance on classifier gener-ation,two kinds of classifier calibration approaches are designed from both prototype-level and example-level aspects.Extensive experiments on five benchmark datasets,including the large-scale iNaturalist,Places-LT,and ImageNet-LT,justify that the proposed PCL can outperform state-of-the-arts.Furthermore,validation experiments can demonstrate the effectiveness of tailored designs in PCL for long-tailed problems.