Papers › FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

8 Dec 2016arXiv:1612.02649archive 2025-07-28

Judy Hoffman, Dequan Wang, Fisher Yu, Trevor Darrell

Fully convolutional models for dense prediction have proven successful for a wide range of visual tasks. Such models perform well in a supervised setting, but performance can be surprisingly poor under domain shifts that appear mild to a human observer. For example, training on one city and testing on another in a different geographic region and/or weather condition may result in significantly degraded performance due to pixel-level distribution shift. In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems. Our method consists of both global and category specific adaptation techniques. Global domain alignment is performed using a novel semantic segmentation network with fully convolutional domain adversarial learning. This initially adapted space then enables category specific adaptation through a generalization of constrained weak learning, with explicit transfer of the spatial layout from the source to the target domains. Our approach outperforms baselines across different settings on multiple large-scale datasets, including adapting across various real city environments, different synthetic sub-domains, from simulated to real environments, and on a novel large-scale dash-cam dataset.

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Code

Wanger-SJTU/FCN-in-the-wild mentioned on GitHubpytorchnot reachable when probed 2026-09-18 — repositories for recent papers often appear after camera-ready report

stu92054/Domain-adaptation-on-segmentation mentioned on GitHubpytorch report

zsano1/MEnet-FCN-wild mentioned on GitHubtf report

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Tasks

Image-to-Image Translation Semantic Segmentation Synthetic-to-Real Translation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Image-to-Image Translation	SYNTHIA Fall-to-Winter	FCNs in the wild	mIoU	59.6	#2 of 2	Archive leaderboard	report
Image-to-Image Translation	SYNTHIA-to-Cityscapes	FCNs in the wild	mIoU (13 classes)	20.2	#28 of 28	Archive leaderboard	report
Synthetic-to-Real Translation	GTAV-to-Cityscapes Labels	FCNs in the wild	mIoU	27.1	#73 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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