Papers › Distribution Alignment: A Unified Framework for Long-tail Visual Recognition
Distribution Alignment: A Unified Framework for Long-tail Visual Recognition
Songyang Zhang, Zeming Li, Shipeng Yan, Xuming He, Jian Sun
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
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Long-tail Learning | ImageNet-LT | DisAlign | Top-1 Accuracy | 53.4 | #43 of 69 | Archive leaderboard | report |
| Long-tail Learning | Places-LT | DisAlign | Top-1 Accuracy | 39.3 | #20 of 29 | Archive leaderboard | report |
| Long-tail Learning | iNaturalist 2018 | DisAlign | Top-1 Accuracy | 70.6% | #30 of 43 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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