Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
6-2020
Abstract
This paper proposes a Hyperbolic Visual Embedding Learning Network for zero-shot recognition. The network learns image embeddings in hyperbolic space, which is capable of preserving the hierarchical structure of semantic classes in low dimensions. Comparing with existing zeroshot learning approaches, the network is more robust because the embedding feature in hyperbolic space better represents class hierarchy and thereby avoid misleading resulted from unrelated siblings. Our network outperforms exiting baselines under hierarchical evaluation with an extremely challenging setting, i.e., learning only from 1,000 categories to recognize 20,841 unseen categories. While under flat evaluation, it has competitive performance as state-of-the-art methods but with five times lower embedding dimensions. Our code is publicly available.
Discipline
Databases and Information Systems | Graphics and Human Computer Interfaces
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, June 13-19
First Page
9270
Last Page
9278
Identifier
10.1109/CVPR42600.2020.00929
Publisher
IEEE Computer Society
City or Country
Virtual Conference
Citation
LIU, Shaoteng; CHEN, Jingjing; PAN, Liangming; NGO, Chong-wah; CHUA, Tat-Seng; and JIANG, Yu-Gang.
Hyperbolic visual embedding learning for zero-shot recognition. (2020). Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, June 13-19. 9270-9278.
Available at: https://ink.library.smu.edu.sg/sis_research/6463
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons