Papers › Semi-Supervised Object Detection with Adaptive Class-Rebalancing Self-Training
Semi-Supervised Object Detection with Adaptive Class-Rebalancing Self-Training
Fangyuan Zhang, Tianxiang Pan, Bin Wang
This study delves into semi-supervised object detection (SSOD) to improve detector performance with additional unlabeled data. State-of-the-art SSOD performance has been achieved recently by self-training, in which training supervision consists of ground truths and pseudo-labels. In current studies, we observe that class imbalance in SSOD severely impedes the effectiveness of self-training. To address the class imbalance, we propose adaptive class-rebalancing self-training (ACRST) with a novel memory module called CropBank. ACRST adaptively rebalances the training data with foreground instances extracted from the CropBank, thereby alleviating the class imbalance. Owing to the high complexity of detection tasks, we observe that both self-training and data-rebalancing suffer from noisy pseudo-labels in SSOD. Therefore, we propose a novel two-stage filtering algorithm to generate accurate pseudo-labels. Our method achieves satisfactory improvements on MS-COCO and VOC benchmarks. When using only 1\% labeled data in MS-COCO, our method achieves 17.02 mAP improvement over supervised baselines, and 5.32 mAP improvement compared with state-of-the-art methods.
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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 0.5% labeled data | Adaptive Rebalancing | mAP | 19.62±0.37 | #2 of 5 | Archive leaderboard | report |
| Semi-Supervised Object Detection | COCO 1% labeled data | Adaptive Class-Rebalancing | mAP | 26.07±0.46 | #3 of 22 | Archive leaderboard | report |
| Semi-Supervised Object Detection | COCO 10% labeled data | Adaptive Class-Rebalancing | mAP | 34.92±0.22 | #13 of 27 | Archive leaderboard | report |
| Semi-Supervised Object Detection | COCO 100% labeled data | Adaptive Class-Rebalancing | mAP | 42.79 | #9 of 13 | Archive leaderboard | report |
| Semi-Supervised Object Detection | COCO 2% labeled data | Adaptive Class-Rebalancing | mAP | 28.69±0.17 | #6 of 19 | Archive leaderboard | report |
| Semi-Supervised Object Detection | COCO 5% labeled data | Adaptive Class-Rebalancing | mAP | 31.35±0.13 | #13 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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