Papers › Webly Supervised Image Classification with Self-Contained Confidence
Webly Supervised Image Classification with Self-Contained Confidence
Jingkang Yang, Litong Feng, Weirong Chen, Xiaopeng Yan, Huabin Zheng, Ping Luo, Wayne Zhang
This paper focuses on webly supervised learning (WSL), where datasets are built by crawling samples from the Internet and directly using search queries as web labels. Although WSL benefits from fast and low-cost data collection, noises in web labels hinder better performance of the image classification model. To alleviate this problem, in recent works, self-label supervised loss ℒₛ is utilized together with webly supervised loss ℒ_w. ℒₛ relies on pseudo labels predicted by the model itself. Since the correctness of the web label or pseudo label is usually on a case-by-case basis for each web sample, it is desirable to adjust the balance between ℒₛ and ℒ_w on sample level. Inspired by the ability of Deep Neural Networks (DNNs) in confidence prediction, we introduce Self-Contained Confidence (SCC) by adapting model uncertainty for WSL setting, and use it to sample-wisely balance ℒₛ and ℒ_w. Therefore, a simple yet effective WSL framework is proposed. A series of SCC-friendly regularization approaches are investigated, among which the proposed graph-enhanced mixup is the most effective method to provide high-quality confidence to enhance our framework. The proposed WSL framework has achieved the state-of-the-art results on two large-scale WSL datasets, WebVision-1000 and Food101-N. Code is available at https://github.com/bigvideoresearch/SCC.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | WebVision-1000 | SCC (ResNet50-D) | ImageNet Top-1 Accuracy | 70.66% | #7 of 16 | Archive leaderboard | report |
| Image Classification | WebVision-1000 | SCC (ResNet50-D) | ImageNet Top-5 Accuracy | 88.46% | #7 of 16 | Archive leaderboard | report |
| Image Classification | WebVision-1000 | SCC (ResNet50-D) | Top-1 Accuracy | 75.78% | #7 of 16 | Archive leaderboard | report |
| Image Classification | WebVision-1000 | SCC (ResNet50-D) | Top-5 Accuracy | 91.07% | #7 of 16 | 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.
Methods
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