Uncertainty-Aware and Class-Balanced Domain Adaptation for Object Detection in Driving Scenes

This work tackles the cross-domain object detection problem which aims to generalize a pre-trained object detector to different domains (driving scenes) without labels. An uncertainty-aware and class-balanced domain adaptation method is proposed based on two motivations: 1) estimation and exploitation of model uncertainty in a new domain is critical for reliable domain adaptation; and 2) in domain adaptation the distribution alignment of two domains as well as the maintaining of category discriminability are both important. In particular, we compose a Bayesian CNN-based framework for uncertainty estimation in object detection. We propose an algorithm for generating uncertainty-aware pseudo-labels, which are then used in uncertainty-guided self-training and category-aware feature alignment. We further devise a scheme with class-balanced memory banks to address the long-tail distribution problem in category-aware feature alignment. Experiments on multiple cross-domain object detection benchmarks show that our proposed method achieves state-of-the-art performance.