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A-Teacher: Asymmetric Network for 3D Semi-Supervised Object Detection
Wang, Hanshi1,2; Zhang, Zhipeng3; Gao, Jin1,2; Hu, Weiming1,2,4
Conference NameIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Conference Date2024-06-17至2024-06-21
Conference PlaceSeattle, United States

This work proposes the first online asymmetric semi- supervised framework, namely A-Teacher, for LiDAR-based 3D object detection. Our motivation stems from the obser- vation that 1) existing symmetric teacher-student methods for semi-supervised 3D object detection have characterized simplicity, but impede the distillation performance between teacher and student because of the demand for an identical model structure and input data format. 2) The offline asym- metric methods with a complex teacher model, constructed differently, can generate more precise pseudo labels, but is challenging to jointly optimize the teacher and student model. Consequently, in this paper, we devise a different path from the conventional paradigm, which can harness the capacity of a strong teacher while preserving the advan- tages of jointly updating the whole framework. The essence is the proposed attention-based refinement model that can be seamlessly integrated into a vanilla teacher. The refine- ment model works in the divide-and-conquer manner that respectively handles three challenging scenarios including 1) objects detected in the current timestamp but with sub- optimal box quality, 2) objects are missed in the current timestamp but are detected in supporting frames, 3) objects are neglected in all frames. It is worth noting that even while tackling these complex cases, our model retains the efficiency of the online semi-supervised framework. Exper- imental results on Waymo [38] show that our method out- performs previous state-of-the-art HSSDA [17] for 4.7 on mAP (L1) while consuming fewer training resources.

Indexed ByEI
Sub direction classification目标检测、跟踪与识别
planning direction of the national heavy laboratory实体人工智能系统感认知
Paper associated data
Document Type会议论文
Corresponding AuthorGao, Jin
Affiliation1.State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), CASIA
2.School of Artificial Intelligence, University of Chinese Academy of Sciences
4.School of Information Science and Technology, ShanghaiTech University
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Hanshi,Zhang, Zhipeng,Gao, Jin,et al. A-Teacher: Asymmetric Network for 3D Semi-Supervised Object Detection[C],2024.
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