Feature Bias Correction: A Feature Augmentation Method for Long-tailed Recognition

The features extracted by the network trained on the long-tailed dataset have significant bias, and the existing methods exhibit poor performance. To alleviate this bias, we propose a Feature Bias Correction (FBC) method, which solves this problem by migrating the biased features back to their correct locations. FBC consists of two core components: Feature Saliency Rebalancing and Similarity Feature Distinguishing. Specifically, Feature Saliency Rebalancing encourages features to be more significant by giving less weight to the feature map, which allows the feature map to represent the sample better. The Similarity Feature Distinguishing module guides the model training by giving more accurate labels to the samples. Finally, our method can be easily combined with the existing long-tailed recognition methods. Experiments on multiple datasets show that our FBC achieves state-of-the-art performance on long-tailed recognition tasks.