Papers › Cross-Domain Semantic Segmentation via Domain-Invariant Interactive Relation Transfer
Cross-Domain Semantic Segmentation via Domain-Invariant Interactive Relation Transfer
Fengmao Lv, Tao Liang, Xiang Chen, Guosheng Lin
Exploiting photo-realistic synthetic data to train semantic segmentation models has received increasing attention over the past years. However, the domain mismatch between synthetic and real images will cause a significant performance drop when the model trained with synthetic images is directly applied to real-world scenarios. In this paper, we propose a new domain adaptation approach, called Pivot Interaction Transfer (PIT). Our method mainly focuses on constructing pivot information that is common knowledge shared across domains as a bridge to promote the adaptation of semantic segmentation model from synthetic domains to real-world domains. Specifically, we first infer the image-level category information about the target images, which is then utilized to facilitate pixel-level transfer for semantic segmentation, with the assumption that the interactive relation between the image-level category information and the pixel-level semantic information is invariant across domains. To this end, we propose a novel multi-level region expansion mechanism that aligns both the image-level and pixel-level information. Comprehensive experiments on the adaptation from both GTAV and SYNTHIA to Cityscapes clearly demonstrate the superiority of our method.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Adaptation | SYNTHIA-to-Cityscapes | PIT (ResNet-101) | mIoU | 44.0 | #25 of 33 | Archive leaderboard | report |
| Domain Adaptation | SYNTHIA-to-Cityscapes | PIT (VGG-16) | mIoU | 38.1 | #32 of 33 | 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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