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Generalized Zero-Shot Learning (GZSL) and Open-Set Recognition (OSR) are twomainstream settings that greatly extend conventional visual object recognition.However, the limitations of their problem settings are not negligible. Thenovel categories in GZSL require pre-defined semantic labels, making theproblem setting less realistic; the oversimplified unknown class in OSR failsto explore the innate fine-grained and mixed structures of novel categories. Inlight of this, we are motivated to consider a new problem setting namedZero-Knowledge Zero-Shot Learning (ZK-ZSL) that assumes no prior knowledge ofnovel classes and aims to classify seen and unseen samples and recover semanticattributes of the fine-grained novel categories for further interpretation. Toachieve this, we propose a novel framework that recovers the clusteringstructures of both seen and unseen categories where the seen class structuresare guided by source labels. In addition, a structural alignment loss isdesigned to aid the semantic learning of unseen categories with their recoveredstructures. Experimental results demonstrate our method's superior performancein classification and semantic recovery on four benchmark datasets.

Zero-Knowledge Zero-Shot Learning for Novel Visual Category Discovery