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...
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.
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