  Despite the recent success of deep neural networks, it remains challenging to
effectively model the long-tail class distribution in 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
propose a unified distribution alignment strategy for long-tail visual
recognition. Specifically, we develop an adaptive calibration function that
enables us to adjust the classification scores for each data point. We then
introduce a generalized re-weight method in the two-stage learning to balance
the class prior, which provides a flexible and unified solution to diverse
scenarios in 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. The code and models will be made publicly
available at: https://github.com/Megvii-BaseDetection/DisAlign
