Local pseudo-attributes for long-tailed recognition

Abstract

Existing long-tailed recognition methods focus on learning global image representation by re-weighing, re-sampling, or global representation learning. However, we observe that solving real-world long-tailed recognition problems requires a fine-grained understanding of local parts within the image in order to avoid confusion among images with similar global configurations. We propose a novel self-supervised learning framework based on local pseudo-attributes (LPA) that are learned via clustering of local features without any human annotations. Such pseudo-attributes are often more balanced compared to image-level class labels. Our method outperforms the state-of-the-art on various long-tailed image classification datasets, such as CIFAR100-LT, iNaturalist, and ImageNet-LT.