Generalized Zero-Shot Learning (GZSL) aims to recognize new categories with auxiliary semantic information,e.g., category attributes. In this paper, we handle the critical issue of domain shift problem, i.e., confusion between seen and unseen categories, by progressively improving cross-domain transferability and category discriminability of visual representations. Our approach, named Dual Progressive Prototype Network (DPPN), constructs two types of prototypes that record prototypical visual patterns for attributes and categories, respectively. With attribute prototypes, DPPN alternately searches attribute-related local regions and updates corresponding attribute prototypes to progressively explore accurate attribute-region correspondence. This enables DPPN to produce visual representations with accurate attribute localization ability, which benefits the semantic-visual alignment and representation transferability. Besides, along with progressive attribute localization, DPPN further projects category prototypes into multiple spaces to progressively repel visual representations from different categories, which boosts category discriminability. Both attribute and category prototypes are collaboratively learned in a unified framework, which makes visual representations of DPPN transferable and distinctive. Experiments on four benchmarks prove that DPPN effectively alleviates the domain shift problem in GZSL.

翻译：零热通用学习(GZSL) 旨在识别带有辅助语义信息的新类别,例如分类属性。在本文件中,我们处理域变换问题的关键问题,即:通过逐步改进跨域可转移性和视觉表现的分类差异,使可见和不可见类别混淆,从而逐步改善视觉表现的跨域可转移性和类别差异性。我们称为“双重进步原型网络”(DPPN) 的方法建立了两种类型原型,分别记录属性和类别的原型,用属性原型,DPPN 替代搜索与属性有关的本地区域,更新相应的属性原型,以逐步探索准确的属性区域对应通信。这使得DPPN能够产生具有准确属性本地化能力的视觉表现,有利于语义-视觉调整和表示的可转移性。此外,除了逐步属性定位外,DPPN还进一步将原型项目分类为多个空间,以逐步从不同类别中复制视觉表现,从而增强差异性。两种属性和类别原型都是在统一的框架内合作学习的,使DPPN可转移和辨别性区域对应原型在统一框架内进行视觉表现。