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We describe an unsupervised domain adaptation method for image content shiftcaused by viewpoint changes for a semantic segmentation task. Most existingmethods perform domain alignment in a shared space and assume that the mappingfrom the aligned space to the output is transferable. However, the novelcontent induced by viewpoint changes may nullify such a space for effectivealignments, thus resulting in negative adaptation. Our method works withoutaligning any statistics of the images between the two domains. Instead, itutilizes a view transformation network trained only on color images tohallucinate the semantic images for the target. Despite the lack ofsupervision, the view transformation network can still generalize to semanticimages thanks to the inductive bias introduced by the attention mechanism.Furthermore, to resolve ambiguities in converting the semantic images tosemantic labels, we treat the view transformation network as a functionalrepresentation of an unknown mapping implied by the color images and proposefunctional label hallucination to generate pseudo-labels in the target domain.Our method surpasses baselines built on state-of-the-art correspondenceestimation and view synthesis methods. Moreover, it outperforms thestate-of-the-art unsupervised domain adaptation methods that utilizeself-training and adversarial domain alignment. Our code and dataset will bemade publicly available.

ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation