  Most existing state-of-the-art video classification methods assume that the
training data obey a uniform distribution. However, video data in the real
world typically exhibit an imbalanced long-tailed class distribution, resulting
in a model bias towards head class and relatively low performance on tail
class. While the current long-tailed classification methods usually focus on
image classification, adapting it to video data is not a trivial extension. We
propose an end-to-end multi-expert distribution calibration method to address
these challenges based on two-level distribution information. The method
jointly considers the distribution of samples in each class (intra-class
distribution) and the overall distribution of diverse data (inter-class
distribution) to solve the issue of imbalanced data under long-tailed
distribution. By modeling the two-level distribution information, the model can
jointly 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.
