Iterative Zero-Shot Localization via Semantic-Assisted Location Network

Yang, Y; Zhao, L; Liu, X

Iterative Zero-Shot Localization via Semantic-Assisted Location Network

Yang, Y Zhao, L

Liu, X

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Robotics and Automation Letters, 2022, 7, (3), pp. 5974-5981
Issue Date:: 2022-07-01

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Field	Value	Language
dc.contributor.author	Yang, Y
dc.contributor.author	Zhao, L https://orcid.org/0000-0003-4063-8183
dc.contributor.author	Liu, X
dc.date.accessioned	2023-03-09T06:08:38Z
dc.date.available	2023-03-09T06:08:38Z
dc.date.issued	2022-07-01
dc.identifier.citation	IEEE Robotics and Automation Letters, 2022, 7, (3), pp. 5974-5981
dc.identifier.issn	2377-3766
dc.identifier.issn	2377-3766
dc.identifier.uri	http://hdl.handle.net/10453/166839
dc.description.abstract	This paper considers zero-shot localization problem where the images used for localization are taken from new locations that are not included in the training dataset. We propose the Semantic-Assisted Location Network (SLN), which considers a new location essentially as a new combination of certain semantic classes. Moreover, we propose an iterative zero-shot learning method based on Expectation-Maximization (EM) algorithm to deal with the problem that the inter-class relationships of class representations in image embedding space and class embedding space are inconsistent. Experiments show that the proposed iterative zero-shot learning method outperforms start-of-the-art zero-shot localization methods by a large margin.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Robotics and Automation Letters
dc.relation.isbasedon	10.1109/LRA.2022.3153715
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0913 Mechanical Engineering
dc.title	Iterative Zero-Shot Localization via Semantic-Assisted Location Network
dc.type	Journal Article
utslib.citation.volume	7
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - RI - Robotics Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-09T06:08:37Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	7
utslib.citation.issue	3

Abstract:

This paper considers zero-shot localization problem where the images used for localization are taken from new locations that are not included in the training dataset. We propose the Semantic-Assisted Location Network (SLN), which considers a new location essentially as a new combination of certain semantic classes. Moreover, we propose an iterative zero-shot learning method based on Expectation-Maximization (EM) algorithm to deal with the problem that the inter-class relationships of class representations in image embedding space and class embedding space are inconsistent. Experiments show that the proposed iterative zero-shot learning method outperforms start-of-the-art zero-shot localization methods by a large margin.

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http://hdl.handle.net/10453/166839