Cross Domain Object Detection

Cross-domain object detection aims to adapt object detectors trained on one dataset (the source domain) to perform well on a different, unlabeled dataset (the target domain), overcoming the challenges posed by differing data distributions. Current research heavily focuses on unsupervised domain adaptation techniques, employing methods like mean teacher frameworks, adversarial learning, and self-training, often within one-stage or transformer-based detector architectures. These advancements are crucial for deploying object detection models in real-world scenarios where labeled data is scarce or expensive to obtain, impacting applications such as autonomous driving and robotics. Furthermore, research is actively exploring improved evaluation metrics and addressing issues like noisy pseudo-labels and class imbalance to enhance the robustness and accuracy of cross-domain detection.