simply treated as background in object detection training, as the number of labeled instances is usually small, the trained model could over-fit the training data severely, and mistaken the unlabeled object as background. To address the issue, we propose a simple yet effective algorithm, named *positiveness-focused object detector* (PFOD), to progressively propagate the labels, which treats unlabeled regions as background first and then neglects them to mainly focus on the positive samples during the training. The intuition is to first learn a compact classifier which might over-fit the data, and then relax the learning by ignoring the unlabeled region to pull the unlabeled instances towards being positive. In this way, bounding box supervision could be automatically expanded during training before we apply a standard object detector.

Overall, our contributions can be summarized as follows.

1.  We explicitly formulate the problem of incompletely-supervised learning, which focuses on the incomplete annotations for object counting.

2.  We propose a progressive label propagation algorithm through positiveness-focused object detector to properly handle the incomplete labels.

3.  We conduct extensive experiments demonstrates the effectiveness of the proposed PFOD to handle such training problems. On the CARPK dataset for parking lot car counting, we improved mAP@0.5 from 