Long-Tailed Multi-Label Visual Recognition by Collaborative Training on Uniform and Re-Balanced Samplings

Abstract

Long-tailed data distribution is common in many multi-label visual recognition tasks and the direct use of these data for training usually leads to relatively low performance on tail classes. While re-balanced data sampling can improve the performance on tail classes, it may also hurt the performance on head classes in training due to label co-occurrence. In this paper, we propose a new approach to train on both uniform and re-balanced samplings in a collaborative way, resulting in performance improvement on both head and tail classes. More specifically, we design a visual recognition network with two branches: one takes the uniform sampling as input while the other takes the re-balanced sampling as the input. For each branch, we conduct visual recognition using a binary-cross-entropy-based classification loss with learnable logit compensation. We further define a new cross-branch loss to enforce the consistency when the same input image goes through the two branches. We conduct extensive experiments on VOC-LT and COCO-LT datasets. The results show that the proposed method significantly outperforms previous state-of-the-art methods on long-tailed multi-label visual recognition.