Papers › Semi-supervised Object Detection via Virtual Category Learning
Semi-supervised Object Detection via Virtual Category Learning
Changrui Chen, Kurt Debattista, Jungong Han
Due to the costliness of labelled data in real-world applications, semi-supervised object detectors, underpinned by pseudo labelling, are appealing. However, handling confusing samples is nontrivial: discarding valuable confusing samples would compromise the model generalisation while using them for training would exacerbate the confirmation bias issue caused by inevitable mislabelling. To solve this problem, this paper proposes to use confusing samples proactively without label correction. Specifically, a virtual category (VC) is assigned to each confusing sample such that they can safely contribute to the model optimisation even without a concrete label. It is attributed to specifying the embedding distance between the training sample and the virtual category as the lower bound of the inter-class distance. Moreover, we also modify the localisation loss to allow high-quality boundaries for location regression. Extensive experiments demonstrate that the proposed VC learning significantly surpasses the state-of-the-art, especially with small amounts of available labels.
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Code
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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 | VC | mAP | 19.46 | #3 of 5 | Archive leaderboard | report |
| Semi-Supervised Object Detection | COCO 1% labeled data | VC | mAP | 23.86 | #8 of 22 | Archive leaderboard | report |
| Semi-Supervised Object Detection | COCO 10% labeled data | VC | detector | FasterRCNN-Res50 | #14 of 27 | Archive leaderboard | report |
| Semi-Supervised Object Detection | COCO 10% labeled data | VC | mAP | 34.82 | #14 of 27 | Archive leaderboard | report |
| Semi-Supervised Object Detection | COCO 2% labeled data | VC | mAP | 27.70 | #10 of 19 | Archive leaderboard | report |
| Semi-Supervised Object Detection | COCO 5% labeled data | VC | mAP | 32.05 | #11 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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