We propose a novel point annotated setting for the weakly semi-supervised object detection task, in which the dataset comprises small fully annotated images and large weakly annotated images by points. It achieves a balance between tremendous annotation burden and detection performance. Based on this setting, we analyze existing detectors and find that these detectors have difficulty in fully exploiting the power of the annotated points. To solve this, we introduce a new detector, Point DETR, which extends DETR by adding a point encoder. Extensive experiments conducted on MS-COCO dataset in various data settings show the effectiveness of our method. In particular, when using 20% fully labeled data from COCO, our detector achieves a promising performance, 33.3 AP, which outperforms a strong baseline (FCOS) by 2.0 AP, and we demonstrate the point annotations bring over 10 points in various AR metrics.

我们提出了一种基于点注释的弱监督目标检测方法，并引入了 Point DETR 模型进行训练，实验结果表明该方法在 MS-COCO 数据集上使用 20% 的样本标注数据时（fully labeled data），取得了 33.3 AP 的性能，比 FCOS 强基线模型高出 2.0 AP；同时，点注释在各类 AR 指标上可提高 10 个点以上。

点作为查询：基于点的弱监督物体检测