  Object detection using single point supervision has received increasing
attention over the years. However, the performance gap between point supervised
object detection (PSOD) and bounding box supervised detection remains large. In
this paper, we attribute such a large performance gap to the failure of
generating high-quality proposal bags which are crucial for multiple instance
learning (MIL). To address this problem, we introduce a lightweight alternative
to the off-the-shelf proposal (OTSP) method and thereby create the Point-to-Box
Network (P2BNet), which can construct an inter-objects balanced proposal bag by
generating proposals in an anchor-like way. By fully investigating the accurate
position information, P2BNet further constructs an instance-level bag, avoiding
the mixture of multiple objects. Finally, a coarse-to-fine policy in a cascade
fashion is utilized to improve the IoU between proposals and ground-truth (GT).
Benefiting from these strategies, P2BNet is able to produce high-quality
instance-level bags for object detection. P2BNet improves the mean average
precision (AP) by more than 50% relative to the previous best PSOD method on
the MS COCO dataset. It also demonstrates the great potential to bridge the
performance gap between point supervised and bounding-box supervised detectors.
The code will be released at github.com/ucas-vg/P2BNet.

Computer Vision and Pattern Recognition