Papers › Rethinking Pseudo Labels for Semi-Supervised Object Detection
Rethinking Pseudo Labels for Semi-Supervised Object Detection
Hengduo Li, Zuxuan Wu, Abhinav Shrivastava, Larry S. Davis
Recent advances in semi-supervised object detection (SSOD) are largely driven by consistency-based pseudo-labeling methods for image classification tasks, producing pseudo labels as supervisory signals. However, when using pseudo labels, there is a lack of consideration in localization precision and amplified class imbalance, both of which are critical for detection tasks. In this paper, we introduce certainty-aware pseudo labels tailored for object detection, which can effectively estimate the classification and localization quality of derived pseudo labels. This is achieved by converting conventional localization as a classification task followed by refinement. Conditioned on classification and localization quality scores, we dynamically adjust the thresholds used to generate pseudo labels and reweight loss functions for each category to alleviate the class imbalance problem. Extensive experiments demonstrate that our method improves state-of-the-art SSOD performance by 1-2% AP on COCO and PASCAL VOC while being orthogonal and complementary to most existing methods. In the limited-annotation regime, our approach improves supervised baselines by up to 10% AP using only 1-10% labeled data from COCO.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Object Detection | COCO 1% labeled data | RPL | mAP | 19.02 ± 0.25 | #18 of 22 | Archive leaderboard | report |
| Semi-Supervised Object Detection | COCO 10% labeled data | RPL | mAP | 32.23± 0.14 | #19 of 27 | Archive leaderboard | report |
| Semi-Supervised Object Detection | COCO 100% labeled data | RPL | mAP | 43.3 | #8 of 13 | Archive leaderboard | report |
| Semi-Supervised Object Detection | COCO 2% labeled data | RPL | mAP | 23.34± 0.18 | #13 of 19 | Archive leaderboard | report |
| Semi-Supervised Object Detection | COCO 5% labeled data | RPL | mAP | 28.4 ± 0.15 | #19 of 23 | 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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