Point-to-Box Network for Accurate Object Detection via Single Point Supervision

• URL: http://arxiv.org/abs/2207.06827v1
• Date: Thu, 14 Jul 2022 11:32:00 GMT
• Title: Point-to-Box Network for Accurate Object Detection via Single Point Supervision
• Authors: Pengfei Chen, Xuehui Yu, Xumeng Han, Najmul Hassan, Kai Wang, Jiachen Li, Jian Zhao, Humphrey Shi, Zhenjun Han, and Qixiang Ye
• Abstract summary: We introduce a lightweight alternative to the off-the-shelf proposal (OTSP) method. P2BNet can construct an inter-objects balanced proposal bag by generating proposals in an anchor-like way. The code will be released at COCO.com/ucas-vg/P2BNet.
• Score: 51.95993495703855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object detection using single point supervision has received increasing attention over the years. 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.

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