Switching Temporary Teachers for Semi-Supervised Semantic Segmentation
Jaemin Na Jung-Woo Ha Hyung Jin Chang Dongyoon Han Wonjun Hwang
The teacher-student framework, prevalent in semi-supervised semantic
segmentation, mainly employs the exponential moving average (EMA) to update a
single teacher's weights based on the student's. However, EMA updates raise a
problem in that the weights of the teacher and student are getting coupled,
causing a potential performance bottleneck. Furthermore, this problem may
become more severe when training with more complicated labels such as
segmentation masks but with few annotated data. This paper introduces Dual
Teacher, a simple yet effective approach that employs dual temporary teachers
aiming to alleviate the coupling problem for the student. The temporary
teachers work in shifts and are progressively improved, so consistently prevent
the teacher and student from becoming excessively close. Specifically, the
temporary teachers periodically take turns generating pseudo-labels to train a
student model and maintain the distinct characteristics of the student model
for each epoch. Consequently, Dual Teacher achieves competitive performance on
the PASCAL VOC, Cityscapes, and ADE20K benchmarks with remarkably shorter
training times than state-of-the-art methods. Moreover, we demonstrate that our
approach is model-agnostic and compatible with both CNN- and Transformer-based
models. Code is available at \url{https://github.com/naver-ai/dual-teacher}.