Dr Yang Long yang.long@durham.ac.uk
Associate Professor
From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis
Long, Yang; Liu, Li; Shao, Ling; Shen, Fumin; Ding, Guiguang; Han, Jungong
Authors
Li Liu
Ling Shao
Fumin Shen
Guiguang Ding
Jungong Han
Citation
Long, Y., Liu, L., Shao, L., Shen, F., Ding, G., & Han, J. (2017, December). From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis. Presented at Computer Vision and Pattern Recognition IEEE
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | Computer Vision and Pattern Recognition IEEE |
Publication Date | 2017 |
Deposit Date | Sep 1, 2019 |
Public URL | https://durham-repository.worktribe.com/output/1142093 |
You might also like
EfficientTDNN: Efficient Architecture Search for Speaker Recognition
(2022)
Journal Article
Kernelized distance learning for zero-shot recognition
(2021)
Journal Article
A plug-in attribute correction module for generalized zero-shot learning
(2020)
Journal Article
Semantic combined network for zero-shot scene parsing
(2019)
Journal Article
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search