Meta-Prototype Decoupled Training for Long-tailed Learning
Long-tailed learning aims to tackle the crucial challenge that head classes dominate the training procedure under severe class imbalance in real-world scenarios. Supervised contrastive learning has turned out to be a worth exploring research direction, which seeks to learn class-specific feature prototypes to enhance long-tailed learning performance. However, little attention has been paid to how to calibrate the empirical prototypes which are severely biased due to the scarce data in tail classes. Without the aid of correct prototypes, these explorations have not shown the significant promise expected. Motivated by this, we propose the meta-prototype contrastive learning to automatically learn the reliable representativeness of prototypes and more discriminative feature space via a meta-learning manner. In addition, on top of the calibrated prototypes, we leverage it to replace the mean of class statistics and predict the targeted distribution of balanced training data. By this procedure, we formulate the feature augmentation algorithm which samples additional features from the predicted distribution and further balances the over-whelming dominance severity of head classes. We summarize the above two stages as the meta-prototype decouple training scheme and conduct a series of experiments to validate the effectiveness of the framework. Our method outperforms previous work with a large margin and achieves state-of-the-art performance on long-tailed image classification and semantic segmentation tasks (e.g., we achieve 55.1% overall accuracy with ResNetXt-50 in ImageNet-LT).