Learning Discriminative Latent Attributes for Zero-Shot Classification
Abstract
Zero-shot learning (ZSL) aims to transfer knowledge from observed classes to the unseen classes, based on the assumption that both the seen and unseen classes share a common semantic space, among which attributes enjoy a great popularity. However, few works study whether the human-designed semantic attributes are discriminative enough to recognize different classes. Moreover, attributes are often correlated with each other, which makes it less desirable to learn each attribute independently. In this paper, we propose to learn a latent attribute space, which is not only discriminative but also semantic-preserving, to perform the ZSL task. Specifically, a dictionary learning framework is exploited to connect the latent attribute space with attribute space and similarity space. Extensive experiments on four benchmark datasets show the effectiveness of the proposed approach.
Cite
Text
Jiang et al. "Learning Discriminative Latent Attributes for Zero-Shot Classification." International Conference on Computer Vision, 2017. doi:10.1109/ICCV.2017.453Markdown
[Jiang et al. "Learning Discriminative Latent Attributes for Zero-Shot Classification." International Conference on Computer Vision, 2017.](https://mlanthology.org/iccv/2017/jiang2017iccv-learning-a/) doi:10.1109/ICCV.2017.453BibTeX
@inproceedings{jiang2017iccv-learning-a,
title = {{Learning Discriminative Latent Attributes for Zero-Shot Classification}},
author = {Jiang, Huajie and Wang, Ruiping and Shan, Shiguang and Yang, Yi and Chen, Xilin},
booktitle = {International Conference on Computer Vision},
year = {2017},
doi = {10.1109/ICCV.2017.453},
url = {https://mlanthology.org/iccv/2017/jiang2017iccv-learning-a/}
}