Papers › Learning to Adapt Structured Output Space for Semantic Segmentation
Learning to Adapt Structured Output Space for Semantic Segmentation
Yi-Hsuan Tsai, Wei-Chih Hung, Samuel Schulter, Kihyuk Sohn, Ming-Hsuan Yang, Manmohan Chandraker
Convolutional neural network-based approaches for semantic segmentation rely on supervision with pixel-level ground truth, but may not generalize well to unseen image domains. As the labeling process is tedious and labor intensive, developing algorithms that can adapt source ground truth labels to the target domain is of great interest. In this paper, we propose an adversarial learning method for domain adaptation in the context of semantic segmentation. Considering semantic segmentations as structured outputs that contain spatial similarities between the source and target domains, we adopt adversarial learning in the output space. To further enhance the adapted model, we construct a multi-level adversarial network to effectively perform output space domain adaptation at different feature levels. Extensive experiments and ablation study are conducted under various domain adaptation settings, including synthetic-to-real and cross-city scenarios. We show that the proposed method performs favorably against the state-of-the-art methods in terms of accuracy and visual quality.
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Code
Syntology Ran 6 of 7 code samples harvested from 2 repositories linked to this paper; 1 has no recorded run. Of those that ran: 3 ran · honoured contract; 3 ran · our draft was wrong.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Adaptation | Synscapes-to-Cityscapes | AdaptSegNet | mIoU | 52.7 | #3 of 3 | Archive leaderboard | report |
| Image-to-Image Translation | SYNTHIA-to-Cityscapes | Multi-level Adaptation | mIoU (13 classes) | 46.7 | #21 of 28 | Archive leaderboard | report |
| Image-to-Image Translation | SYNTHIA-to-Cityscapes | Single-level Adaptation | mIoU (13 classes) | 45.9 | #23 of 28 | Archive leaderboard | report |
| Synthetic-to-Real Translation | GTAV-to-Cityscapes Labels | AdaptSegNet(multi-level) | mIoU | 42.4 | #64 of 73 | Archive leaderboard | report |
| Synthetic-to-Real Translation | SYNTHIA-to-Cityscapes | AdaptSegNet(Multi-level) | MIoU (13 classes) | 46.7 | #38 of 38 | 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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