Distribution Alignment: A Unified Framework for Long-Tail Visual Recognition

Songyang Zhang, Zeming Li, Shipeng Yan, Xuming He, Jian Sun

Abstract: Despite the success of the deep neural networks, it remains challenging to effectively build a system for long-tail visual recognition tasks. To address this problem, we first investigate the performance bottleneck of the two-stage learning framework via ablative study. Motivated by our discovery, we develop a unified distribution alignment strategy for long-tail visual recognition. Particularly, we first propose an adaptive calibration strategy for each data point to calibrate its classification scores. Then we introduce a generalized re-weight method to incorporate the class prior, which provides a flexible and unified solution to copy with diverse scenarios of various visual recognition tasks. We validate our method by extensive experiments on four tasks, including image classification, semantic segmentation, object detection, and instance segmentation. Our approach achieves the state-of-the-art results across all four recognition tasks with a simple and unified framework.