  As with other deep learning methods, label quality is important for learning
modern convolutional object detectors. However, the potentially large number
and wide diversity of object instances that can be found in complex image
scenes makes constituting complete annotations a challenging task; objects
missing annotations can be observed in a variety of popular object detection
datasets. These missing annotations can be problematic, as the standard
cross-entropy loss employed to train object detection models treats
classification as a positive-negative (PN) problem: unlabeled regions are
implicitly assumed to be background. As such, any object missing a bounding box
results in a confusing learning signal, the effects of which we observe
empirically. To remedy this, we propose treating object detection as a
positive-unlabeled (PU) problem, which removes the assumption that unlabeled
regions must be negative. We demonstrate that our proposed PU classification
loss outperforms the standard PN loss on PASCAL VOC and MS COCO across a range
of label missingness, as well as on Visual Genome and DeepLesion with full
labels.
