Papers › ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Matthieu Cord, Patrick Pérez
Semantic segmentation is a key problem for many computer vision tasks. While approaches based on convolutional neural networks constantly break new records on different benchmarks, generalizing well to diverse testing environments remains a major challenge. In numerous real world applications, there is indeed a large gap between data distributions in train and test domains, which results in severe performance loss at run-time. In this work, we address the task of unsupervised domain adaptation in semantic segmentation with losses based on the entropy of the pixel-wise predictions. To this end, we propose two novel, complementary methods using (i) entropy loss and (ii) adversarial loss respectively. We demonstrate state-of-the-art performance in semantic segmentation on two challenging "synthetic-2-real" set-ups and show that the approach can also be used for detection.
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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 | Panoptic SYNTHIA-to-Cityscapes | ADVENT | mPQ | 28.1 | #5 of 5 | Archive leaderboard | report |
| Domain Adaptation | Panoptic SYNTHIA-to-Mapillary | ADVENT | mPQ | 18.3 | #5 of 5 | Archive leaderboard | report |
| Domain Adaptation | SYNTHIA-to-Cityscapes | ADVENT (ResNet-101) | mIoU | 41.2 | #27 of 33 | Archive leaderboard | report |
| Image-to-Image Translation | GTAV-to-Cityscapes Labels | ADVENT | mIoU | 44.8 | #19 of 22 | Archive leaderboard | report |
| Image-to-Image Translation | SYNTHIA-to-Cityscapes | ADVENT | mIoU (13 classes) | 48 | #20 of 28 | Archive leaderboard | report |
| Synthetic-to-Real Translation | GTAV-to-Cityscapes Labels | AdvEnt(with MinEnt) | mIoU | 45.5 | #59 of 73 | 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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