# Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotations Source: [Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotations]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:42:13.334Z Authors: Yongtao Ge, Qiang Zhou, Xinlong Wang, Zhibin Wang, Hao Li, Chunhua Shen Published: 2022-06-01T07:04:38.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()