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.

翻译：广义零样本学习(GZSL)和开放集识别(OSR)是两种显著扩展传统视觉物体识别的主流设定。然而，其问题设定存在不可忽视的局限性：GZSL中的新类别需要预定义的语义标签，导致问题设定缺乏现实性；OSR中过度简化的未知类别未能充分探索新类别固有的细粒度混合结构。基于此，我们提出一种名为零知识零样本学习(ZK-ZSL)的新问题设定，该设定假设对新类别无先验知识，旨在分类可见与不可见样本，并恢复细粒度新类别的语义属性以进行深层解释。为实现该目标，我们提出一种新颖框架，通过源标签引导可见类别结构，同时恢复可见与不可见类别的聚类结构。此外，我们设计了一种结构对齐损失函数，利用恢复后的结构辅助不可见类别的语义学习。实验结果表明，该方法在四个基准数据集上的分类与语义恢复性能均表现优越。