Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotations

• URL: http://arxiv.org/abs/2206.00274v1
• Date: Wed, 1 Jun 2022 07:04:38 GMT
• Title: Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotations
• Authors: Yongtao Ge, Qiang Zhou, Xinlong Wang, Chunhua Shen, Zhibin Wang, Hao Li
• Abstract summary: We present Point-Teaching, a weakly semi-supervised object detection framework. Specifically, we propose a Hungarian-based point matching method to generate pseudo labels for point annotated images. We propose a simple-yet-effective data augmentation, termed point-guided copy-paste, to reduce the impact of the unmatched points.
• Score: 81.02347863372364
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Point annotations are considerably more time-efficient than bounding box annotations. However, how to use cheap point annotations to boost the performance of semi-supervised object detection remains largely unsolved. In this work, we present Point-Teaching, a weakly semi-supervised object detection framework to fully exploit the point annotations. Specifically, we propose a Hungarian-based point matching method to generate pseudo labels for point annotated images. We further propose multiple instance learning (MIL) approaches at the level of images and points to supervise the object detector with point annotations. Finally, we propose a simple-yet-effective data augmentation, termed point-guided copy-paste, to reduce the impact of the unmatched points. Experiments demonstrate the effectiveness of our method on a few datasets and various data regimes.

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