Robust Recognition

Robust recognition aims to develop computer vision systems that accurately identify objects and scenes despite variations in viewing conditions, data corruption, or adversarial attacks. Current research focuses on improving model robustness through techniques like configural processing (emphasizing spatial relationships), part-based models (leveraging object components), and the use of high-frequency features or topological representations. These advancements are crucial for deploying reliable vision systems in real-world applications such as robotics, autonomous driving, and medical imaging, where robustness to noise and uncertainty is paramount.