Cross-Domain Object Detection by Dual Adaptive Branch

The object detection task usually assumes that the training and test samples obey the same distribution, and this assumption is not valid in reality, therefore the study of cross-domain object detection is proposed. Compared with image classification, the cross-domain object detection task presents the greater challenge, which requires both accurate classification and localization of samples in the target domain. The teacher–student framework (the student model is supervised by pseudo-labels from the teacher model) has produced a large accuracy improvement in cross-domain object detection. Feature-level adversarial training is used in the student model, which allows features in the source and target domains to share a similar distribution. However, the direction and gradient of the weights can be divided into domain-specific and domain-invariant features, and the purpose of domain adaptive is to focus on the domain-invariant features while eliminating interference from the domain-specific features. Inspired by this, we propose a teacher–student framework named dual adaptive branch (DAB), which uses domain adversarial learning to address the domain distribution. Specifically, we ensure that the student model aligns domain-invariant features and suppresses domain-specific features in this process. We further validate our method based on multiple domains. The experimental results demonstrate that our proposed method significantly improves the performance of cross-domain object detection and achieves the competitive experimental results on common benchmarks.