Dual Progressive Prototype Network for Generalized Zero-Shot Learning

TL;DR

This paper introduces the Dual Progressive Prototype Network (DPPN), a novel method for generalized zero-shot learning that progressively improves attribute localization and category discriminability to address domain shift issues.

Contribution

DPPN constructs and collaboratively learns attribute and category prototypes with progressive localization and separation, enhancing transferability and discriminability in GZSL.

Findings

DPPN outperforms existing methods on four benchmarks.

It effectively alleviates the domain shift problem.

Prototypes improve attribute localization and category separation.

Abstract

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.