bt-vMF Contrastive and Collaborative Learning for Long-Tailed Visual Recognition

Real-world data often exhibit long tail distributions with heavy class imbalance, where the majority (head) classes can dominate the training process and alter the decision boundaries of the minority (tail) classes, leading to biased feature spaces. Recently, researchers have investigated the potential of contrastive learning for long-tailed visual recognition and introduced a class-balanced factor in loss function engineering. Although this method can help improve performance, it harms head performance due to undesirable bias, resulting in poor separability of minority samples in feature spaces. In this paper, we target the logit adjustment and propose balanced student-t von Mises-Fisher (bt-vMF) contrastive learning, encouraging a large margin between the head and tail classes and providing better generalization. In addition, the network trained on long-tailed datasets suffers from great uncertainty in predictions. To alleviate this issue, we build mutual supervision among multiple experts via proposed bilateral collaborative learning (BCL), in which the collaboration is conducted from both bt-vMF similarity and relationship distillation. Simply put, our designs focus on the generalization power of a single expert and the knowledge transfer among multiple experts to alleviate the biased feature space and uncertainty in long-tailed learning, respectively. Experiments on multiple datasets show that our method achieves competitive performance on long-tailed visual recognition task.