Periodically Exchange Teacher-Student for Source-Free Object Detection
Qipeng Liu Luojun Lin Zhifeng Shen Zhifeng Yang
Source-free object detection (SFOD) aims to adapt the source detector to
unlabeled target domain data in the absence of source domain data. Most SFOD
methods follow the same self-training paradigm using mean-teacher (MT)
framework where the student model is guided by only one single teacher model.
However, such paradigm can easily fall into a training instability problem that
when the teacher model collapses uncontrollably due to the domain shift, the
student model also suffers drastic performance degradation. To address this
issue, we propose the Periodically Exchange Teacher-Student (PETS) method, a
simple yet novel approach that introduces a multiple-teacher framework
consisting of a static teacher, a dynamic teacher, and a student model. During
the training phase, we periodically exchange the weights between the static
teacher and the student model. Then, we update the dynamic teacher using the
moving average of the student model that has already been exchanged by the
static teacher. In this way, the dynamic teacher can integrate knowledge from
past periods, effectively reducing error accumulation and enabling a more
stable training process within the MT-based framework. Further, we develop a
consensus mechanism to merge the predictions of two teacher models to provide
higher-quality pseudo labels for student model. Extensive experiments on
multiple SFOD benchmarks show that the proposed method achieves
state-of-the-art performance compared with other related methods, demonstrating
the effectiveness and superiority of our method on SFOD task.