DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection

• URL: http://arxiv.org/abs/2207.05536v1
• Date: Tue, 12 Jul 2022 13:54:54 GMT
• Title: DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection
• Authors: Gang Li, Xiang Li, Yujie Wang, Yichao Wu, Ding Liang, Shanshan Zhang
• Abstract summary: Mean-Teacher (MT) scheme is widely adopted in semi-supervised object detection (SSOD) In this paper, we propose the Inverse NMS Clustering (INC) and Rank Matching (RM) to instantiate the dense supervision.
• Score: 42.75316070378037
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Mean-Teacher (MT) scheme is widely adopted in semi-supervised object detection (SSOD). In MT, the sparse pseudo labels, offered by the final predictions of the teacher (e.g., after Non Maximum Suppression (NMS) post-processing), are adopted for the dense supervision for the student via hand-crafted label assignment. However, the sparse-to-dense paradigm complicates the pipeline of SSOD, and simultaneously neglects the powerful direct, dense teacher supervision. In this paper, we attempt to directly leverage the dense guidance of teacher to supervise student training, i.e., the dense-to-dense paradigm. Specifically, we propose the Inverse NMS Clustering (INC) and Rank Matching (RM) to instantiate the dense supervision, without the widely used, conventional sparse pseudo labels. INC leads the student to group candidate boxes into clusters in NMS as the teacher does, which is implemented by learning grouping information revealed in NMS procedure of the teacher. After obtaining the same grouping scheme as the teacher via INC, the student further imitates the rank distribution of the teacher over clustered candidates through Rank Matching. With the proposed INC and RM, we integrate Dense Teacher Guidance into Semi-Supervised Object Detection (termed DTG-SSOD), successfully abandoning sparse pseudo labels and enabling more informative learning on unlabeled data. On COCO benchmark, our DTG-SSOD achieves state-of-the-art performance under various labelling ratios. For example, under 10% labelling ratio, DTG-SSOD improves the supervised baseline from 26.9 to 35.9 mAP, outperforming the previous best method Soft Teacher by 1.9 points.

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