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Harvesting dense pixel-level annotations to train deep neural networks forsemantic segmentation is extremely expensive and unwieldy at scale. Whilelearning from synthetic data where labels are readily available soundspromising, performance degrades significantly when testing on novel realisticdata due to domain discrepancies. We present Dual Channel-wise AlignmentNetworks (DCAN), a simple yet effective approach to reduce domain shift at bothpixel-level and feature-level. Exploring statistics in each channel of CNNfeature maps, our framework performs channel-wise feature alignment, whichpreserves spatial structures and semantic information, in both an imagegenerator and a segmentation network. In particular, given an image from thesource domain and unlabeled samples from the target domain, the generatorsynthesizes new images on-the-fly to resemble samples from the target domain inappearance and the segmentation network further refines high-level featuresbefore predicting semantic maps, both of which leverage feature statistics ofsampled images from the target domain. Unlike much recent and concurrent workrelying on adversarial training, our framework is lightweight and easy totrain. Extensive experiments on adapting models trained on syntheticsegmentation benchmarks to real urban scenes demonstrate the effectiveness ofthe proposed framework.

DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation