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Instance contrast for unsupervised representation learning has achieved greatsuccess in recent years. In this work, we explore the idea of instancecontrastive learning in unsupervised domain adaptation (UDA) and propose anovel Category Contrast technique (CaCo) that introduces semantic priors on topof instance discrimination for visual UDA tasks. By considering instancecontrastive learning as a dictionary look-up operation, we construct asemantics-aware dictionary with samples from both source and target domainswhere each target sample is assigned a (pseudo) category label based on thecategory priors of source samples. This allows category contrastive learning(between target queries and the category-level dictionary) forcategory-discriminative yet domain-invariant feature representations: samplesof the same category (from either source or target domain) are pulled closerwhile those of different categories are pushed apart simultaneously. ExtensiveUDA experiments in multiple visual tasks (e.g., segmentation, classificationand detection) show that CaCo achieves superior performance as compared withstate-of-the-art methods. The experiments also demonstrate that CaCo iscomplementary to existing UDA methods and generalizable to other learningsetups such as unsupervised model adaptation, open-/partial-set adaptation etc.

Category Contrast for Unsupervised Domain Adaptation in Visual Tasks