Pith. sign in

REVIEW 6 cited by

A Simple Semi-Supervised Learning Framework for Object Detection

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
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/.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 6 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

  1. SCOUT: Semi-supervised Camouflaged Object Detection by Utilizing Text and Adaptive Data Selection

    cs.CV 2025-08 unverdicted novelty 6.0 of 10

    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

    cs.CV 2025-07 conditional novelty 6.0 of 10

    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

    cs.CV 2025-08 conditional novelty 5.0 of 10

    An ensemble-based soft pseudo-labeling pipeline improves waste detection on the ZeroWaste dataset, beating fully supervised training with the same labeled images.

  4. Dual Guidance Semi-Supervised Action Detection

    cs.CV 2025-07 conditional novelty 5.0 of 10

    Dual guidance, combining frame-level action classification with box-level prediction, improves pseudo-box selection for semi-supervised spatio-temporal action localization.

  5. SemiHMER: Semi-supervised Handwritten Mathematical Expression Recognition using pseudo-labels

    cs.CV 2025-02 conditional novelty 5.0 of 10

    SemiHMER combines dual-branch pseudo-supervision, weak-to-strong augmentation, and a dynamic counting module to improve handwritten math formula recognition on CROHME benchmarks.

  6. 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.

Pith tools