This paper presents an end-to-end semi-supervised object detection approach, in contrast to previous more complex multi-stage methods. The end-to-end training gradually improves pseudo label qualities during the curriculum, and the more and more accurate pseudo labels in turn benefit object detection training. We also propose two simple yet effective techniques within this framework: a soft teacher mechanism where the classification loss of each unlabeled bounding box is weighed by the classification score produced by the teacher network; a box jittering approach to select reliable pseudo boxes for the learning of box regression. On COCO benchmark, the proposed approach outperforms previous methods by a large margin under various labeling ratios, i.e. 1\%, 5\% and 10\%. Moreover, our approach proves to perform also well when the amount of labeled data is relatively large. For example, it can improve a 40.9 mAP baseline detector trained using the full COCO training set by +3.6 mAP, reaching 44.5 mAP, by leveraging the 123K unlabeled images of COCO. On the state-of-the-art Swin Transformer-based object detector (58.9 mAP on test-dev), it can still significantly improve the detection accuracy by +1.5 mAP, reaching 60.4 mAP, and improve the instance segmentation accuracy by +1.2 mAP, reaching 52.4 mAP, pushing the new state-of-the-art.
翻译:与以往较为复杂的多阶段方法相比,本文件提出了一个端到端半监督的物体探测方法。端到端培训逐渐改进课程中的假标签质量,并逐渐改进更准确的假标签质量,进而使目标探测培训受益。我们在此框架内还提出了两个简单而有效的技术:一个软教师机制,将每个未贴标签的捆绑盒的分类损失由教师网络所制作的分类分数加权;一个盒式抽动方法,为学习箱回归选择可靠的假箱。在COCO基准方面,拟议的方法在各种标签比率(即1 ⁇ 、5 ⁇ 和10 ⁇ )下,以大差优优于先前的方法。此外,当标签数据数量相对较大时,我们的方法也证明效果良好。例如,它能够利用以+3.6 mAP为基准的完全COCO培训来改进40.9 mAP基线探测器,达到44.5 mAP,利用COCO的123K无标签图像。 在Swinerver+AP为基地的物体探测达到60.589 mAP的状态上,通过测试改进了它达到新的精确度。