Open-Set Recognition
Overview
Open-set recognition studies how models can classify known categories without forcing unfamiliar inputs into those categories, a need in settings such as medical diagnosis, security, and ecological monitoring. Common approaches use calibrated confidence, energy or distance scores, metric learning and prototypes; newer systems also use vision-language models, retrieval, and explicit negative examples. Work spans images, video, audio, text, biometrics, and object detection, with methods for rejecting unknowns as well as discovering and learning new classes. A central challenge is balancing accurate known-class predictions with reliable rejection under domain shift, while calibrating thresholds and evaluating them without access to representative unknown data.
Latest papers
69 papers9 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.