Mean Teacher with Stochastic Restoration and Contrastive Learning for Domain Adaptive Object Detection

Test-time adaptation enables a pre-trained model to update its weights during inference, in order to adapt to a target domain that has a different distribution from the source domain. This adaptation occurs without any supervision and often in a more challenging source-free setting where no data from the source domain is used. While much attention has been given towards test-time adaptation for classification tasks, the significance of domain adaptation extends to various other applications and particularly object detection. Many studies consider a static target domain, which fails to simulate real-world conditions. In real-world applications, target domain is non-stationary and the target distribution can gradually change over time. In this work, we focus on continual test-time adaptation scenario, in which the target domain is continually changing over time. Leveraging the existing mean-teacher framework for object detection, we stochastically restore a small part of the student’s weights to the source pre-trained model weights during adaptation. Additionally, we aim to enhance performance by using contrastive learning. After a consistent experimental work it is shown that our proposed method compares favorably with the state of the art.

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