Supervised Object Detection

Supervised object detection aims to train computer vision models to accurately identify and locate objects within images or videos using labeled training data. Current research heavily focuses on improving efficiency and accuracy by exploring alternative supervision strategies, such as weakly supervised, semi-supervised, and even single-point supervision, to reduce the reliance on expensive, fully annotated datasets. These efforts leverage techniques like contrastive learning, pseudo-label generation, and novel data augmentation methods to enhance model performance. The resulting advancements have significant implications for various applications, including autonomous driving, medical image analysis, and remote sensing.