  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.
