Abstract
In multi-domain long-tailed learning, data imbalance appears in two ways: within-domain class imbalance and across-domain sample proportion variation. These imbalances introduce biases in covariates and representations when learning domain-invariant features in both input and latent spaces. This paper applies an advanced reweighting balanced representation learning (BRL) algorithm to multi-domain long-tailed image recognition. By integrating covariate and representation balancing techniques into a reweighting-based class balancing approach, BRL effectively addresses these biases. Extensive evaluation on six benchmark datasets confirms its ability to extract domain- and class-unbiased feature representations, leading to excellent classifier performance, especially for the hardest classes. This approach also shows potential for applications in areas such as environmental monitoring and medical imaging, providing a robust solution with broad scientific implications.