Papers › ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer

ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer

25 Apr 2022arXiv:2204.11891archive 2025-07-28

Shahaf Ettedgui, Shady Abu-Hussein, Raja Giryes

Using synthetic data for training neural networks that achieve good performance on real-world data is an important task as it can reduce the need for costly data annotation. Yet, synthetic and real world data have a domain gap. Reducing this gap, also known as domain adaptation, has been widely studied in recent years. Closing the domain gap between the source (synthetic) and target (real) data by directly performing the adaptation between the two is challenging. In this work, we propose a novel two-stage framework for improving domain adaptation techniques on image data. In the first stage, we progressively train a multi-scale neural network to perform image translation from the source domain to the target domain. We denote the new transformed data as "Source in Target" (SiT). Then, we insert the generated SiT data as the input to any standard UDA approach. This new data has a reduced domain gap from the desired target domain, which facilitates the applied UDA approach to close the gap further. We emphasize the effectiveness of our method via a comparison to other leading UDA and image-to-image translation techniques when used as SiT generators. Moreover, we demonstrate the improvement of our framework with three state-of-the-art UDA methods for semantic segmentation, HRDA, DAFormer and ProDA, on two UDA tasks, GTA5 to Cityscapes and Synthia to Cityscapes.

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Tasks

Domain Adaptation Image-to-Image Translation Semantic Segmentation Style Transfer Synthetic-to-Real Translation Translation Unsupervised Domain Adaptation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Domain Adaptation	GTA5 to Cityscapes	DAFormer + ProCST	mIoU	69.4	#9 of 28	Archive leaderboard	report
Domain Adaptation	SYNTHIA-to-Cityscapes	DAFormer + ProCST	mIoU	61.6	#10 of 33	Archive leaderboard	report
Image-to-Image Translation	GTAV-to-Cityscapes Labels	DAFormer + ProCST	mIoU	69.4	#7 of 22	Archive leaderboard	report
Image-to-Image Translation	SYNTHIA-to-Cityscapes	DAFormer + ProCST	mIoU (13 classes)	68.2	#6 of 28	Archive leaderboard	report
Semantic Segmentation	GTAV-to-Cityscapes Labels	DAFormer + ProCST	mIoU	69.4	#5 of 12	Archive leaderboard	report
Semantic Segmentation	SYNTHIA-to-Cityscapes	DAFormer + ProCST	Mean IoU	61.6	#5 of 7	Archive leaderboard	report
Synthetic-to-Real Translation	GTAV-to-Cityscapes Labels	DAFormer + ProCST	mIoU	69.4	#10 of 73	Archive leaderboard	report
Synthetic-to-Real Translation	SYNTHIA-to-Cityscapes	DAFormer + ProCST	MIoU (16 classes)	61.6	#7 of 38	Archive leaderboard	report
Unsupervised Domain Adaptation	GTAV-to-Cityscapes Labels	DAFormer + ProCST	mIoU	69.4	#9 of 20	Archive leaderboard	report
Unsupervised Domain Adaptation	SYNTHIA-to-Cityscapes	DAFormer + ProCST	mIoU (13 classes)	68.2	#8 of 23	Archive leaderboard	report

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