Papers › ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

30 Nov 2018CVPR 2019 6arXiv:1811.12833archive 2025-07-28

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

Domain AdaptationImage-to-Image TranslationSegmentationSemantic SegmentationSynthetic-to-Real TranslationUnsupervised Domain Adaptation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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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