A Novel Unsupervised Domain Adaption Method for Depth-Guided Semantic Segmentation Using Coarse-to-Fine Alignment

Domain adaptation methods in machine learning deal with the domain shift issue by aligning source and target data representation. This paper proposes a novel domain adaptation method for semantic segmentation that exploits the Fourier transform on chromatic space to improve the quality of style transfer, and generates pseudo-labels for self-training by combining the results from different teachers obtained at different rounds of self-training. Our method also applies class-level adversarial learning to achieve a more fine-grained alignment between the two domains, and a late fusion with a depth-estimation model to improve its segmentation outputs. Experiments show that our method yields superior performance in terms of accuracy compared to other existing state-of-the-art methods.

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Domain Adaptation GTA5 to Cityscapes FAFS mIoU 58.8 # 18
Unsupervised Domain Adaptation GTAV-to-Cityscapes Labels FAFS mIoU 58.8 # 14
Unsupervised Domain Adaptation SYNTHIA-to-Cityscapes FAFS mIoU (13 classes) 61.4 # 12
mIoU 54.5 # 8


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