Papers › Weight-guided class complementing for long-tailed image recognition
Weight-guided class complementing for long-tailed image recognition
Xinqiao Zhao, Jimin Xiao, Siyue Yu, Hui Li, Bingfeng Zhang
Real-world data are often long-tailed distributed and have plenty classes. This characteristic leads to a significant performance drop for various models. One reason behind that is the gradient shift caused by unsampled classes in each training iteration. In this paper, we propose a Weight-Guided Class Complementing framework to address this issue. Specifically, this framework first complements the unsampled classes in each training iteration by using a dynamic updated data slot. Then, considering the over-fitting issue caused by class complementing, we utilize the classifier weights as learned knowledge and encourage the model to discover more class specific characteristics. Finally, we design a weight refining scheme to deal with the long-tailed bias existing in classifier weights. Experimental results show that our framework can be implemented upon different existing approaches effectively, achieving consistent improvements on various benchmarks with new state-of-the-art performances.
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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 | CIFAR-10-LT (ρ=100) | NCL* + WGCC (ensemble) | Error Rate | 15.4 | #12 of 28 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-100-LT (ρ=100) | NCL* + WGCC (ensemble) | Error Rate | 44.9 | #12 of 66 | Archive leaderboard | report |
| Long-tail Learning | CIFAR-100-LT (ρ=100) | LDAM-DRW + WGCC | Error Rate | 56.4 | #55 of 66 | 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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