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Unsupervised domain adaptive object detection aims to adapt a well-traineddetector from its original source domain with rich labeled data to a new targetdomain with unlabeled data. Recently, mainstream approaches perform this taskthrough adversarial learning, yet still suffer from two limitations. First,they mainly align marginal distribution by unsupervised cross-domain featurematching, and ignore each feature's categorical and positional information thatcan be exploited for conditional alignment; Second, they treat all classes asequally important for transferring cross-domain knowledge and ignore thatdifferent classes usually have different transferability. In this paper, wepropose a joint adaptive detection framework (JADF) to address the abovechallenges. First, an end-to-end joint adversarial adaptation framework forobject detection is proposed, which aligns both marginal and conditionaldistributions between domains without introducing any extra hyperparameter.Next, to consider the transferability of each object class, a metric forclass-wise transferability assessment is proposed, which is incorporated intothe JADF objective for domain adaptation. Further, an extended study fromunsupervised domain adaptation (UDA) to unsupervised few-shot domain adaptation(UFDA) is conducted, where only a few unlabeled training images are availablein unlabeled target domain. Extensive experiments validate that JADF iseffective in both the UDA and UFDA settings, achieving significant performancegains over existing state-of-the-art cross-domain detection methods.

Joint Distribution Alignment via Adversarial Learning for Domain Adaptive Object Detection