Stochastic Context Consistency Reasoning for Domain Adaptive Object Detection

Domain Adaptive Object Detection (DAOD) aims to improve the adaptation of the detector for the unlabeled target domain by the labeled source domain. Recent advances leverage a self-training framework to enable a student model to learn the target domain knowledge using pseudo labels generated by a teacher model. Despite great successes, such category-level consistency supervision suffers from poor quality of pseudo labels to fully explore the contextual target domain knowledge. To mitigate the problem, we propose a stochastic context consistency reasoning network with the self-training framework. Firstly, we introduce a stochastic complementary masking module (SCM) to generate complementary masked images thus preventing the network from over-relying on specific visual clues. Secondly, we design an inter-changeable context consistency reasoning module (Inter-CCR), which constructs an inter-context consistency paradigm to capture the texture and contour details in the target domain by aligning the predictions of the student model for complementary masked images. Meanwhile, we develop an intra-changeable context consistency reasoning module (Intra-CCR), which constructs an intra-context consistency paradigm to strengthen the utilization of context relations by utilizing pseudo labels to supervise the predictions of the student model. Experimental results on three DAOD benchmarks demonstrate our method outperforms current state-of-the-art methods by a large margin. Code is released at https://github.com/HDUyiming/SOCCER.