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.

本文介绍了一种名为 “双重渐进式原型网络（DPPN）” 的方法，用于解决广义零样本学习（GZSL）中的问题，包括域偏移问题、视觉代表性的跨域可转移性和类别可辨别性。DPPN 通过学习属性和类别的两种原型模式来构建视觉表征，从而逐渐提高属性定位准确性和类别区分度，并在四个基准测试上进行了验证。