Learning Muti-expert Distribution Calibration for Long-tailed Video Classification

Most existing state-of-the-art video classification methods assume the training data obey a uniform distribution. However, video data in the real world typically exhibit long-tail class distribution and imbalance, which extensively results in a model bias towards head class and leads to relatively low performance on tail class. While the current long-tail classification methods usually focus on image classification, adapting it to video data is not a trivial extension. We propose an end-to-end multi-experts distribution calibration method based on two-level distribution information to address these challenges. The method jointly considers the distribution of samples in each class (intra-class distribution) and the diverse distributions of overall data (inter-class distribution) to solve the problem of imbalanced data under long-tailed distribution. By modeling this two-level distribution information, the model can consider the head classes and the tail classes and significantly transfer the knowledge from the head classes to improve the performance of the tail classes. Extensive experiments verify that our method achieves state-of-the-art performance on the long-tailed video classification task.