Abstract
Generalized Zero-Shot Learning (GZSL) and Open-Set Recognition (OSR) are two
mainstream settings that greatly extend conventional visual object recognition.
However, the limitations of their problem settings are not negligible. The
novel categories in GZSL require pre-defined semantic labels, making the
problem setting less realistic; the oversimplified unknown class in OSR fails
to explore the innate fine-grained and mixed structures of novel categories. In
light of this, we are motivated to consider a new problem setting named
Zero-Knowledge Zero-Shot Learning (ZK-ZSL) that assumes no prior knowledge of
novel classes and aims to classify seen and unseen samples and recover semantic
attributes of the fine-grained novel categories for further interpretation. To
achieve this, we propose a novel framework that recovers the clustering
structures of both seen and unseen categories where the seen class structures
are guided by source labels. In addition, a structural alignment loss is
designed to aid the semantic learning of unseen categories with their recovered
structures. Experimental results demonstrate our method's superior performance
in classification and semantic recovery on four benchmark datasets.