Balanced MSE for Imbalanced Visual Regression
This addresses the challenge of imbalanced regression in computer vision, which is more complex than classification due to continuous labels, offering a general solution for high-dimensional cases.
The authors tackled the problem of data imbalance in visual regression tasks, such as age and pose estimation, by proposing Balanced MSE, a novel loss function that improves model generalizability and fairness, achieving state-of-the-art results on synthetic and real-world benchmarks.
Data imbalance exists ubiquitously in real-world visual regressions, e.g., age estimation and pose estimation, hurting the model's generalizability and fairness. Thus, imbalanced regression gains increasing research attention recently. Compared to imbalanced classification, imbalanced regression focuses on continuous labels, which can be boundless and high-dimensional and hence more challenging. In this work, we identify that the widely used Mean Square Error (MSE) loss function can be ineffective in imbalanced regression. We revisit MSE from a statistical view and propose a novel loss function, Balanced MSE, to accommodate the imbalanced training label distribution. We further design multiple implementations of Balanced MSE to tackle different real-world scenarios, particularly including the one that requires no prior knowledge about the training label distribution. Moreover, to the best of our knowledge, Balanced MSE is the first general solution to high-dimensional imbalanced regression. Extensive experiments on both synthetic and three real-world benchmarks demonstrate the effectiveness of Balanced MSE.