High-Performance Light Field Reconstruction with Channel-wise and SAI-wise Attention

Hu, Z; Chung, YY; Zandavi, SM; Ouyang, W; He, X; Gao, Y

High-Performance Light Field Reconstruction with Channel-wise and SAI-wise Attention

Hu, Z Chung, YY Zandavi, SM Ouyang, W He, X

Gao, Y

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Publication Type:: Conference Proceeding
Citation:: Communications in Computer and Information Science, 2019, 1143 CCIS pp. 118 - 126
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Hu, Z	en_US
dc.contributor.author	Chung, YY	en_US
dc.contributor.author	Zandavi, SM	en_US
dc.contributor.author	Ouyang, W	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Gao, Y	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	Communications in Computer and Information Science, 2019, 1143 CCIS pp. 118 - 126	en_US
dc.identifier.isbn	9783030368012	en_US
dc.identifier.issn	1865-0929	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138767
dc.description.abstract	© Springer Nature Switzerland AG 2019. Light field (LF) images provide rich information and are suitable for high-level computer vision applications. To acquire capabilities of modeling the correlated information of LF, most of the previous methods have to stack several convolutional layers to improve the feature representation and result in heavy computation and large model sizes. In this paper, we propose channel-wise and SAI-wise attention modules to enhance the feature representation at a low cost. The channel-wise attention module helps to focus on important channels while the SAI-wise attention module guides the network to pay more attention to informative SAIs. The experimental results demonstrate that the baseline network can achieve better performance with the aid of the attention modules.	en_US
dc.relation.ispartof	Communications in Computer and Information Science	en_US
dc.relation.isbasedon	10.1007/978-3-030-36802-9_14	en_US
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in [insert journal title]. The final authenticated version is available online at: https://link.springer.com/chapter/10.1007%2F978-3-030-36802-9_14]”
dc.rights	info:eu-repo/semantics/openAccess
dc.title	High-Performance Light Field Reconstruction with Channel-wise and SAI-wise Attention	en_US
dc.type	Conference Proceeding
utslib.citation.volume	1143 CCIS	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	1143 CCIS	en_US

Abstract:

© Springer Nature Switzerland AG 2019. Light field (LF) images provide rich information and are suitable for high-level computer vision applications. To acquire capabilities of modeling the correlated information of LF, most of the previous methods have to stack several convolutional layers to improve the feature representation and result in heavy computation and large model sizes. In this paper, we propose channel-wise and SAI-wise attention modules to enhance the feature representation at a low cost. The channel-wise attention module helps to focus on important channels while the SAI-wise attention module guides the network to pay more attention to informative SAIs. The experimental results demonstrate that the baseline network can achieve better performance with the aid of the attention modules.

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http://hdl.handle.net/10453/138767