Open-World Vision

Open-World Vision (OWV) emphasizes realistic opportunities and challenges in developing and deploying computer vision systems in the dynamic, vast, and unpredictable real open world, which offers abundant data that can benefit training and challenge testing. It contrasts the traditional "closed-world" paradigm of visual learning and inference, which assumes fixed, known data distributions and categorical labels. Models developed under such closed-world assumptions tend to be brittle when encountering ever-changing and novel scenarios in the real open world. Modern visual learning has shifted towards an open-world paradigm, such as pretraining foundation models on massive data sourced from the open world (e.g., web-sourced data). While these models show unprecedented performance and strong adaptability to downstream tasks, they inherit biases from their open-world pretraining data and can still fail in truly novel or underrepresented scenarios during deployment. This workshop aims not only to uncover current limitations, potential risks, emerging opportunities, and unresolved challenges of open-world vision, but also to solicit solutions that advance the field toward more robust, fair, and adaptable visual systems.