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A Simple Semi-Supervised Learning Framework for Object Detection

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arxiv 2005.04757 v2 pith:E5TSYKOP submitted 2020-05-10 cs.CV

Kihyuk Sohn , Zizhao Zhang , Chun-Liang Li , Han Zhang , Chen-Yu Lee , Tomas Pfister This is my paper · ORCID
classification cs.CV
keywords datastacdetectionlearningms-cocoobjectsemi-supervisedbeen
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abstract

Semi-supervised learning (SSL) has a potential to improve the predictive performance of machine learning models using unlabeled data. Although there has been remarkable recent progress, the scope of demonstration in SSL has mainly been on image classification tasks. In this paper, we propose STAC, a simple yet effective SSL framework for visual object detection along with a data augmentation strategy. STAC deploys highly confident pseudo labels of localized objects from an unlabeled image and updates the model by enforcing consistency via strong augmentations. We propose experimental protocols to evaluate the performance of semi-supervised object detection using MS-COCO and show the efficacy of STAC on both MS-COCO and VOC07. On VOC07, STAC improves the AP$^{0.5}$ from $76.30$ to $79.08$; on MS-COCO, STAC demonstrates $2{\times}$ higher data efficiency by achieving 24.38 mAP using only 5\% labeled data than supervised baseline that marks 23.86\% using 10\% labeled data. The code is available at https://github.com/google-research/ssl_detection/.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 326 citations worldwide. Full citation record

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    SCOUT improves semi-supervised camouflaged object detection by fusing camouflage-related text knowledge with an adaptive data-selection strategy, and contributes a new text-annotated dataset, RefTextCOD.

  2. De-Simplifying Pseudo Labels to Enhancing Domain Adaptive Object Detection

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    DeSimPL reduces the share of easy pseudo-labels during self-labeling domain-adaptive detection, improving SimROD by 2 to 5 mAP on four benchmarks.

  3. Robust and Label-Efficient Deep Waste Detection

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  4. Dual Guidance Semi-Supervised Action Detection

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  5. SST: Self-training with Self-adaptive Thresholding for Semi-supervised Learning

    cs.LG 2025-05 conditional novelty 4.0 of 10

    SST uses per-class thresholds updated once per cycle to pick pseudo-labels, reporting 84.9% ImageNet top-1 accuracy with 10% labeled data on a huge ViT.

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