has a high overlap (Intersection-over-Union $>$ 0.2 in experiments) with any of the original bounding boxes in $L^s$.

After $S$ stages, we feed the training images and the expanded bounding box set $L^{S+1}$ into a normal object detector training pipeline, in which the bounding boxes in $L^{S+1}$ are positive while all the rest are negative. We still apply the YoloV2 algorithm here for training and testing. Ideally, if all the unlabeled object instances could be propagated, the trained model should be able to achieve an accuracy on par with that trained from the full annotations.

Experiments
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Settings
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### Datasets.

We mainly evaluate the approaches on the CARPK [@HsiehLH17] dataset, which contains 989 training images and 459 testing images. The task is to detect and count the car instances in the image. Each training image has $42.7\pm 15.7$ annotated cars, while each testing image has $103.5 \pm 39.1$ cars. The images are collected by a drone on top of car parks. An example image is shown in the first row of Fig. \[fig:isl\]

Another interesting application is to count the number of drinks or products on retail store shelves. To demonstrate the effectiveness of the incompletely-supervised learning algorithm on 