Papers › Dual-level Interaction for Domain Adaptive Semantic Segmentation
Dual-level Interaction for Domain Adaptive Semantic Segmentation
Dongyu Yao, Boheng Li
Self-training approach recently secures its position in domain adaptive semantic segmentation, where a model is trained with target domain pseudo-labels. Current advances have mitigated noisy pseudo-labels resulting from the domain gap. However, they still struggle with erroneous pseudo-labels near the boundaries of the semantic classifier. In this paper, we tackle this issue by proposing a dual-level interaction for domain adaptation (DIDA) in semantic segmentation. Explicitly, we encourage the different augmented views of the same pixel to have not only similar class prediction (semantic-level) but also akin similarity relationship with respect to other pixels (instance-level). As it's impossible to keep features of all pixel instances for a dataset, we, therefore, maintain a labeled instance bank with dynamic updating strategies to selectively store the informative features of instances. Further, DIDA performs cross-level interaction with scattering and gathering techniques to regenerate more reliable pseudo-labels. Our method outperforms the state-of-the-art by a notable margin, especially on confusing and long-tailed classes. Code is available at \href{https://github.com/RainJamesY/DIDA}
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Domain Adaptation | GTAV-to-Cityscapes Labels | DIDA | mIoU | 71.0 | #6 of 20 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | SYNTHIA-to-Cityscapes | DIDA | MIoU (16 classes) | 63.3 | #7 of 23 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | SYNTHIA-to-Cityscapes | DIDA | mIoU | 63.3 | #7 of 23 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | SYNTHIA-to-Cityscapes | DIDA | mIoU (13 classes) | 70.1 | #7 of 23 | 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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