CVPR 2023 - Contrastive Mean Teacher for Domain Adaptive Object Detectors

In this episode we discuss Contrastive Mean Teacher for Domain Adaptive Object Detectors by Shengcao Cao, Dhiraj Joshi, Liang-Yan Gui, Yu-Xiong Wang. The paper proposes a unified framework called Contrastive Mean Teacher (CMT) that integrates mean-teacher self-training and contrastive learning to overcome the domain gap in object detection. CMT extracts object-level features using low-quality pseudo-labels and optimizes them via contrastive learning without requiring labels in the target domain. The proposed framework achieves a new state-of-the-art target-domain performance of 51.9% mAP on Foggy Cityscapes, outperforming the best previous method by 2.1% mAP.