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As with other deep learning methods, label quality is important for learningmodern convolutional object detectors. However, the potentially large numberand wide diversity of object instances that can be found in complex imagescenes makes constituting complete annotations a challenging task; objectsmissing annotations can be observed in a variety of popular object detectiondatasets. These missing annotations can be problematic, as the standardcross-entropy loss employed to train object detection models treatsclassification as a positive-negative (PN) problem: unlabeled regions areimplicitly assumed to be background. As such, any object missing a bounding boxresults in a confusing learning signal, the effects of which we observeempirically. To remedy this, we propose treating object detection as apositive-unlabeled (PU) problem, which removes the assumption that unlabeledregions must be negative. We demonstrate that our proposed PU classificationloss outperforms the standard PN loss on PASCAL VOC and MS COCO across a rangeof label missingness, as well as on Visual Genome and DeepLesion with fulllabels.

Object Detection as a Positive-Unlabeled Problem