Bilateral-Branch Network for Imbalanced Visual Regression

Abstract

Imbalanced visual regression is a practical and pressing issue, but current research is in its early stages. We propose an end-to-end Bilateral-Branch Network (BILBN) for dealing with imbalanced visual regression tasks. The BILBN consists of feature learning and regressor learning branches. The cumulative learning strategy is employed to gradually transition from feature learning to regressor learning in the BILBN model. Furthermore, we propose the Balanced MSESPL loss function, which allows the feature learning to learn simple features first and then progress to learn difficult ones. We also use feature distribution smoothing in the feature learning branch to learn a better feature representation. Compared with feature learning, regressor learning is quite simple, and we only use absolute error in the regressor branch. Finally, extensive experiments are conducted on the IMDB-WIKI-DIR and AgeDB-DIR to show the efficiency and superiority of our proposed methods and the BILBN model.