  Recent semi-supervised object detection (SSOD) has achieved remarkable
progress by leveraging unlabeled data for training. Mainstream SSOD methods
rely on Consistency Regularization methods and Exponential Moving Average
(EMA), which form a cyclic data flow. However, the EMA updating training
approach leads to weight coupling between the teacher and student models. This
coupling in a cyclic data flow results in a decrease in the utilization of
unlabeled data information and the confirmation bias on low-quality or
erroneous pseudo-labels. To address these issues, we propose the Collaboration
of Teachers Framework (CTF), which consists of multiple pairs of teacher and
student models for training. In the learning process of CTF, the Data
Performance Consistency Optimization module (DPCO) informs the best pair of
teacher models possessing the optimal pseudo-labels during the past training
process, and these most reliable pseudo-labels generated by the best performing
teacher would guide the other student models. As a consequence, this framework
greatly improves the utilization of unlabeled data and prevents the positive
feedback cycle of unreliable pseudo-labels. The CTF achieves outstanding
results on numerous SSOD datasets, including a 0.71% mAP improvement on the 10%
annotated COCO dataset and a 0.89% mAP improvement on the VOC dataset compared
to LabelMatch and converges significantly faster. Moreover, the CTF is
plug-and-play and can be integrated with other mainstream SSOD methods.
