Papers › Switching Temporary Teachers for Semi-Supervised Semantic Segmentation
Switching Temporary Teachers for Semi-Supervised Semantic Segmentation
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 https://github.com/naver-ai/dual-teacher.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Semantic Segmentation | Cityscapes 12.5% labeled | Dual Teacher | Validation mIoU | 78.4 | #6 of 33 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 25% labeled | Dual Teacher (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | Validation mIoU | 79.46 | #6 of 30 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 50% labeled | Dual Teacher | Validation mIoU | 80.52 | #4 of 23 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Cityscapes 6.25% labeled | Dual Teacher (DeepLab v3+ with ResNet-101 pretraind on ImageNet-1K) | Validation mIoU | 76.81 | #7 of 18 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | PASCAL VOC 2012 25% labeled | Dual Teacher | Validation mIoU | 81.03 | #2 of 27 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 12.5% labeled | Dual Teacher | Validation mIoU | 81.19 | #7 of 38 | Archive leaderboard | report |
| Semi-Supervised Semantic Segmentation | Pascal VOC 2012 6.25% labeled | Dual Teacher | Validation mIoU | 78.82 | #8 of 19 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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