NDLI: Zero-shot multi-label learning via label factorisation

Content Provider	IET Digital Library
Author	Shao, Hang Guo, Yuchen Ding, Guiguang Han, Jungong
Abstract	This study considers the zero-shot learning problem under the multi-label setting where each test sample is associated with multiple labels that are unseen in training data. The authors propose a novel learning framework based on label factorisation for this problem. Specifically, the authors’ framework takes three key issues into consideration and addresses them in a unified way. The first is knowledge transfer that utilises information from seen classes to build recognition models for unseen classes. The second is label correlation which means that labels which have different semantics may co-occur frequently. This is an important issue in multi-label learning. The authors propose to learn a shared latent space by label factorisation and use the label semantics as the decoding function, which can address both issues. The third is the predictability which requires the learned latent space to be strongly related to the visual features. It is guaranteed by incorporating a regression model into the learning framework. The authors derive two specific formulations from the general framework and propose the corresponding learning algorithms. The authors conducted extensive experiments on three multi-label data sets. The results demonstrated the effectiveness.
Starting Page	117
Ending Page	124
Page Count	8
ISSN	17519632
Volume Number	13
e-ISSN	17519640
Issue Number	Issue 2, Mar (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5131
Journal	IET Computer Vision
Publisher Date	2018-09-27
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Authors Computer Vision And Image Processing Technique Corresponding Learning Algorithm Knowledge Engineering Technique Label Correlation Label Factorisation Label Semantics Learned Latent Space Learning in AI Multilabel Data Sets Multilabel Setting Multiple Labels Novel Learning Framework Regression Analysis Statistics Zero-shot Learning Problem Zero-shot Multilabel Learning
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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