Generalized Category Discovery

Generalized Category Discovery (GCD) tackles the challenge of classifying images into known and novel categories simultaneously, using limited labeled data. Current research focuses on developing robust models that leverage techniques like contrastive learning, teacher-student frameworks, and multimodal information (combining visual and textual data) to improve classification accuracy for both seen and unseen classes, often employing Gaussian Mixture Models or prototypical networks. This area is significant because it addresses the limitations of traditional supervised learning in open-world scenarios, with potential applications in areas like open-world image recognition, scientific data analysis, and robotics.