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.
翻译:微弱监督对象探测(WSOD)是一项任务,它利用只受过图像水平说明培训的模型来探测图像中的物体。目前最先进的模型受益于自监督的实验级监督,但因为监管不力并不包括计数或位置信息,最常用的“argmax”标签方法往往忽略了许多物体。为了缓解这一问题,我们提议了一种新的多实例标签方法,称为物体发现。我们进一步引入了一种在微弱监督之下新的对比性损失,因为没有实例级信息可供取样使用,称为微弱监督的对比性损失(WSCL )。WSCL的目的是通过利用同一类中嵌入矢量矢量的一致特征,为物体发现建立一个可信的相似性阈值。结果之一是,我们在MS-CO 2014 和 2017 以及 PCAL VOC 2012 和 PCAL VOC 上取得了新的最新状态结果,并在PSCAL VOC 2007 上取得了竞争结果。