Balanced Representation Learning for Long-tailed Skeleton-based Action Recognition

Skeleton-based action recognition has recently made significant progress. However, data imbalance is still a great challenge in real-world scenarios. The performance of current action recognition algorithms declines sharply when training data suffers from heavy class imbalance. The imbalanced data actually degrades the representations learned by these methods and becomes the bottleneck for action recognition. How to learn unbiased representations from imbalanced action data is the key to long-tailed action recognition.

基于骨架的动作识别近年来已取得显著进展。然而，在实际场景中，数据不平衡仍是一大挑战。当训练数据存在严重类别不平衡时，现有动作识别算法的性能会急剧下降。不平衡数据实际上会降低这些方法所学习到的表征质量，并成为动作识别的瓶颈。如何从不平衡动作数据中学习无偏表征，是长尾动作识别的关键所在。