Rivisting Source-Free Domain Adaptation Object Detection in Thresholds

Source-free domain adaptive object detection (SFOD) aims to transfer models pre-trained on the source domain to the unlabeled target domain without requiring access to the source data. Most existing SFOD methods leverage pseudo-labels for self-supervised training in the target domain. We investigate the limitations of threshold techniques to obtain high-quality pseudo-labels. In response, we design the Sequential SourceFree domain adaptive Object Detection (S-SFOD) algorithm, which enhances the quality of pseudo-labels at both the image and instance levels. At the image level, we reconstruct the training dataset, prioritizing the training of images that yield more reliable pseudo-labels to help the model acquire valuable target domain knowledge in the initial training stages. At the instance level, we introduce an adaptive local-global threshold method to balance the quality and quantity of pseudo-labels by dynamically adjusting the thresholds based on the model's learning progress. By improving the quality of pseudo-labels through these complementary techniques at both the image and instance levels, we effectively transfer knowledge from the source domain to the target domain. Extensive experiments on multiple cross-domain object detection datasets demonstrate that our proposed method outperforms current state-of-the-art SFOD algorithms. The code and model will be released.