Open World Object

Open-world object detection (OWOD) aims to build object detection systems that can identify both known and unknown objects, and incrementally learn to recognize new objects encountered during operation, unlike traditional closed-set systems. Current research focuses on improving the detection and classification of unknown objects, often employing techniques like knowledge distillation from large vision-language models, semi-supervised learning, and novel architectures such as transformer-based detectors and those incorporating geometric cues. OWOD's significance lies in its potential to create more robust and adaptable computer vision systems for real-world applications like autonomous driving and robotics, where encountering unexpected objects is inevitable.