Open World Object Detection

Open-world object detection (OWOD) aims to enable computer vision systems to identify both known and unknown objects within images, a significant step beyond traditional object detection which assumes a closed set of known classes. Current research focuses on developing models that can accurately detect novel objects, incrementally learn their representations without forgetting previously learned classes, and handle this task efficiently, often leveraging transformer architectures and large pre-trained models like CLIP or Vision Foundation Models. OWOD's advancements are crucial for building robust and adaptable AI systems in real-world scenarios, impacting applications such as autonomous driving, robotics, and anomaly detection across diverse domains.