Source Free Object Detection

Source-free object detection (SFOD) addresses the challenge of adapting a pre-trained object detector to a new, unlabeled domain without access to the original training data. Current research heavily focuses on self-training methods, often employing teacher-student architectures and variations of the Mean Teacher framework, with refinements targeting pseudo-label generation and management to mitigate the impact of noisy predictions. This area is significant because it enables object detection in scenarios with data privacy concerns or limited data availability, impacting applications ranging from remote sensing to autonomous driving.