Papers › Object Discovery via Contrastive Learning for Weakly Supervised Object Detection

Object Discovery via Contrastive Learning for Weakly Supervised Object Detection

16 Aug 2022arXiv:2208.07576archive 2025-07-28

Jinhwan Seo, Wonho Bae, Danica J. Sutherland, Junhyug Noh, Daijin Kim

Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using a model trained only on image-level annotations. Current state-of-the-art models benefit from self-supervised instance-level supervision, but since weak supervision does not include count or location information, the most common ``argmax'' labeling method often ignores many instances of objects. To alleviate this issue, we propose a novel multiple instance labeling method called object discovery. We further introduce a new contrastive loss under weak supervision where no instance-level information is available for sampling, called weakly supervised contrastive loss (WSCL). WSCL aims to construct a credible similarity threshold for object discovery by leveraging consistent features for embedding vectors in the same class. As a result, we achieve new state-of-the-art results on MS-COCO 2014 and 2017 as well as PASCAL VOC 2012, and competitive results on PASCAL VOC 2007.

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Code

jinhseo/od-wscl officialmentioned in paperpytorch report

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Tasks

Contrastive Learning Object Object Detection Object Discovery Weakly Supervised Object Detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Weakly Supervised Object Detection	MS-COCO-2014	OD-WSCL	AP	13.7	#7 of 7	Archive leaderboard	report
Weakly Supervised Object Detection	MS-COCO-2017	OD-WSCL	AP	13.6	#1 of 1	Archive leaderboard	report
Weakly Supervised Object Detection	PASCAL VOC 2007	OD-WSCL	MAP	56.1	#9 of 41	Archive leaderboard	report
Weakly Supervised Object Detection	PASCAL VOC 2012 test	OD-WSCL	MAP	54.6	#7 of 32	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.

Methods

Supervised Contrastive Loss

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