Guest editorial: Visual domain adaptation and generalisation

Qi Wang; Jun Yu; Tongliang Liu; Weifeng Liu

doi:10.1049/iet-cvi.2019.0018

Guest editorial: Visual domain adaptation and generalisation

Qi Wang
, Jun Yu
, Tongliang Liu
, Weifeng Liu

Northwestern Polytechnical University Xian
Hangzhou Dianzi University
The University of Sydney
China University of Petroleum

Research output: Contribution to journal › Review article › peer-review

Abstract

The papers selected for this Special Issue highlight the great breadth of research in visual domain adaption and generalisation today, and the greater potential for this area of Computer Vision in the future. We would like to thank all the reviewers and the IET Editorial Office for their help and support that made this Special Issue possible. We hope the papers in this issue will benefit both experts and newcomers who are interested in domain adaption and generalisation.

Original language	English
Pages (from-to)	87-89
Number of pages	3
Journal	IET Computer Vision
Volume	13
Issue number	2
DOIs	https://doi.org/10.1049/iet-cvi.2019.0018
State	Published - 1 Mar 2019
Externally published	Yes

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10.1049/iet-cvi.2019.0018

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title = "Guest editorial: Visual domain adaptation and generalisation",

abstract = "The papers selected for this Special Issue highlight the great breadth of research in visual domain adaption and generalisation today, and the greater potential for this area of Computer Vision in the future. We would like to thank all the reviewers and the IET Editorial Office for their help and support that made this Special Issue possible. We hope the papers in this issue will benefit both experts and newcomers who are interested in domain adaption and generalisation.",

author = "Qi Wang and Jun Yu and Tongliang Liu and Weifeng Liu",

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year = "2019",

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AB - The papers selected for this Special Issue highlight the great breadth of research in visual domain adaption and generalisation today, and the greater potential for this area of Computer Vision in the future. We would like to thank all the reviewers and the IET Editorial Office for their help and support that made this Special Issue possible. We hope the papers in this issue will benefit both experts and newcomers who are interested in domain adaption and generalisation.

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Guest editorial: Visual domain adaptation and generalisation

Abstract

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