End-to-End Semi-Supervised Object Detection with Soft Teacher

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. Our approach proves to perform also well when the amount of labeled data is relatively large pushing the new state-of-the-art. 2021: Mengde Xu, Zheng Zhang, Han Hu, Jianfeng Wang, Lijuan Wang, Fangyun Wei, X. Bai, Zicheng Liu https://arxiv.org/pdf/2106.09018v3.pdf