Improving Generalization via Attribute Selection on Out-of-the-box Data

no code implementations • 26 Jul 2019 • Xiaofeng Xu, Ivor W. Tsang, Chuancai Liu

Unfortunately, previous attribute selection methods are conducted based on the seen data, and their selected attributes have poor generalization capability to the unseen data, which is unavailable in the training stage of ZSL tasks.

Attribute Zero-Shot Learning

Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation

no code implementations • 20 May 2019 • Xiaofeng Xu, Ivor W. Tsang, Xiaofeng Cao, Ruiheng Zhang, Chuancai Liu

In most of existing attribute-based research, class-specific attributes (CSA), which are class-level annotations, are usually adopted due to its low annotation cost for each class instead of each individual image.

Attribute Diversity

Complementary Attributes: A New Clue to Zero-Shot Learning

no code implementations • 17 Apr 2018 • Xiaofeng Xu, Ivor W. Tsang, Chuancai Liu

Extensive experiments on five ZSL benchmark datasets and the large-scale ImageNet dataset demonstrate that the proposed complementary attributes and rank aggregation can significantly and robustly improve existing ZSL methods and achieve the state-of-the-art performance.

Attribute Style Generalization +1