Self Supervised Object Detection

Self-supervised object detection aims to train object detectors without relying on manually labeled data, leveraging the vast amounts of unlabeled images available. Current research focuses on developing novel pretext tasks and training strategies, including hierarchical clustering, multi-task learning, and contrastive learning, often implemented within transformer-based or convolutional neural network architectures. These advancements improve the efficiency and performance of object detection, particularly in scenarios with limited labeled data, and are impacting various applications requiring robust and scalable object recognition. The development of reliable uncertainty quantification and calibration methods is also a key area of focus, enhancing the safety and trustworthiness of object detectors in real-world deployments.