Shrink,Separate and Aggregate:A Feature Balancing Method for Long-tailed Visual Recognition
Data in the real world often exhibits a long-tailed distribution,where a few classes have a large number of samples,while most classes have only a few samples.This data imbalance can lead to overfitting in the model trained on this dataset for tail classes with fewer samples.To address this problem,we propose a feature balancing method for long-tailed visual recognition,which enhances the model's ability to recognize hard samples by shrink-ing,separating and aggregating samples in the feature space.The method consists of two modules:Feature balance factor and hard sample feature constraint.The feature balance factor uses the sample number of classes to adjust the model's output probability distribution,making the feature distance between different classes more balanced,thereby improving the model's classification accuracy.The hard sample feature constraint performs clustering ana-lysis on the sample features,increasing the boundary distance between different classes,enabling the model to find a more reasonable decision boundary.We conduct experiments on several common long-tailed benchmark datasets,experimental results show that the proposed method not only improves the model's overall classification accuracy on long-tailed data,but also significantly enhances the recognition performance of tail classes.Compared with baseline method BS,the proposed method achieves performance improvements of 7.40％,6.60％ and 2.89％ on CI-FAR100-LT,ImageNet-LT and iNaturalist 2018 datasets respectively.
万方数据
维普
