Object Discovery via Contrastive Learning for Weakly Supervised Object Detection

• URL: http://arxiv.org/abs/2208.07576v1
• Date: Tue, 16 Aug 2022 07:36:12 GMT
• Title: Object Discovery via Contrastive Learning for Weakly Supervised Object Detection
• Authors: Jinhwan Seo, Wonho Bae, Danica J. Sutherland, Junhyug Noh, Daijin Kim
• Abstract summary: Weakly Supervised Object Detection is a task that detects objects in an image using a model trained only on image-level annotations. We propose a novel multiple instance labeling method called object discovery. 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.
• Score: 12.822548027334328
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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