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- Publisher Website: 10.1109/CVPR.2018.00918
- Scopus: eid_2-s2.0-85062870034
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Conference Paper: Semi-Parametric Image Synthesis
Title | Semi-Parametric Image Synthesis |
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Authors | |
Issue Date | 2018 |
Citation | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018, p. 8808-8816 How to Cite? |
Abstract | © 2018 IEEE. We present a semi-parametric approach to photographic image synthesis from semantic layouts. The approach combines the complementary strengths of parametric and nonparametric techniques. The nonparametric component is a memory bank of image segments constructed from a training set of images. Given a novel semantic layout at test time, the memory bank is used to retrieve photographic references that are provided as source material to a deep network. The synthesis is performed by a deep network that draws on the provided photographic material. Experiments on multiple semantic segmentation datasets show that the presented approach yields considerably more realistic images than recent purely parametric techniques. |
Persistent Identifier | http://hdl.handle.net/10722/281970 |
ISSN | 2020 SCImago Journal Rankings: 4.658 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Qi, Xiaojuan | - |
dc.contributor.author | Chen, Qifeng | - |
dc.contributor.author | Jia, Jiaya | - |
dc.contributor.author | Koltun, Vladlen | - |
dc.date.accessioned | 2020-04-09T09:19:16Z | - |
dc.date.available | 2020-04-09T09:19:16Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018, p. 8808-8816 | - |
dc.identifier.issn | 1063-6919 | - |
dc.identifier.uri | http://hdl.handle.net/10722/281970 | - |
dc.description.abstract | © 2018 IEEE. We present a semi-parametric approach to photographic image synthesis from semantic layouts. The approach combines the complementary strengths of parametric and nonparametric techniques. The nonparametric component is a memory bank of image segments constructed from a training set of images. Given a novel semantic layout at test time, the memory bank is used to retrieve photographic references that are provided as source material to a deep network. The synthesis is performed by a deep network that draws on the provided photographic material. Experiments on multiple semantic segmentation datasets show that the presented approach yields considerably more realistic images than recent purely parametric techniques. | - |
dc.language | eng | - |
dc.relation.ispartof | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | - |
dc.title | Semi-Parametric Image Synthesis | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/CVPR.2018.00918 | - |
dc.identifier.scopus | eid_2-s2.0-85062870034 | - |
dc.identifier.spage | 8808 | - |
dc.identifier.epage | 8816 | - |
dc.identifier.isi | WOS:000457843608101 | - |
dc.identifier.issnl | 1063-6919 | - |