  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.
