论文标题
学习长尾视频分类的Muti-Expert分布校准
Learning Muti-expert Distribution Calibration for Long-tailed Video Classification
论文作者
论文摘要
大多数现有的最新视频分类方法假设训练数据遵守统一的分布。但是，现实世界中的视频数据通常表现出不平衡的长尾班分布，从而导致模型偏见对头等阶级，并且在尾巴上的性能相对较低。尽管当前的长尾分类方法通常集中在图像分类上，但将其调整到视频数据并不是一个微不足道的扩展。我们提出了一种端到端的多专家分布校准方法，以根据两级分布信息来应对这些挑战。该方法共同考虑了每个类别中样品的分布（类内部分布）和各种数据（类间分布）的总体分布，以解决在长尾分布下数据不平衡数据的问题。通过对两级分配信息进行建模，该模型可以共同考虑头等阶层和尾部类别，并将知识从头类别转移到尾部类别的性能中。广泛的实验验证了我们的方法是否在长尾视频分类任务上实现了最先进的性能。
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.
