Domain-Adaptive Object Detection via Uncertainty-Aware Distribution Alignment

Domain adaptation aims to transfer knowledge from the sourcedata with annotations to scarcely-labeled data in the target domain,which has attracted a lot of attention in recent years and facilitatedmany multimedia applications. Recent approaches have shown theeffectiveness of using adversarial learning to reduce the distribu-tion discrepancy between the source and target images by aligningdistribution between source and target images at both image and in-stance levels. However, this remains challenging since two domainsmay have distinct background scenes and different objects. More-over, complex combinations of objects and a variety of image stylesdeteriorate the unsupervised cross-domain distribution alignment.To address these challenges, in this paper, we design an end-to-endapproach for unsupervised domain adaptation of object detector.Specifically, we propose a Multi-level Entropy Attention Alignment(MEAA) method that consists of two main components: (1) LocalUncertainty Attentional Alignment (LUAA) module to acceleratethe model better perceiving structure-invariant objects of interestby utilizing information theory to measure the uncertainty of eachlocal region via the entropy of the pixel-wise domain classifierand (2) Multi-level Uncertainty-Aware Context Alignment (MUCA)module to enrich domain-invariant information of relevant objectsbased on the entropy of multi-level domain classifiers. The proposedMEAA is evaluated in four domain-shift object detection scenarios.Experiment results demonstrate state-of-the-art performance onthree challenging scenarios and competitive performance on onebenchmark dataset.