Semi Supervised Object Detection

Semi-supervised object detection (SSOD) aims to improve object detection models by leveraging both labeled and unlabeled data, reducing the reliance on expensive manual annotation. Current research focuses on refining pseudo-labeling techniques to mitigate noise and class imbalance, often employing teacher-student frameworks with various architectures, including CNNs and transformers, and exploring strategies like consistency regularization and active learning. These advancements are significant because they enable the training of high-performing object detectors with substantially less labeled data, impacting fields like medical imaging, remote sensing, and autonomous driving.