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