Semantic attribute matching networks

Seungryong Kim; Dongbo Min; Somi Jeong; Sunok Kim; Sangryul Jeon; Kwanghoon Sohn

doi:10.1109/CVPR.2019.01262

Semantic attribute matching networks

Seungryong Kim, Dongbo Min, Somi Jeong, Sunok Kim, Sangryul Jeon, Kwanghoon Sohn

Department of Computer Science & Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

31 Scopus citations

Abstract

We present semantic attribute matching networks (SAM-Net) for jointly establishing correspondences and transferring attributes across semantically similar images, which intelligently weaves the advantages of the two tasks while overcoming their limitations. SAM-Net accomplishes this through an iterative process of establishing reliable correspondences by reducing the attribute discrepancy between the images and synthesizing attribute transferred images using the learned correspondences. To learn the networks using weak supervisions in the form of image pairs, we present a semantic attribute matching loss based on the matching similarity between an attribute transferred source feature and a warped target feature. With SAM-Net, the state-of-the-art performance is attained on several benchmarks for semantic matching and attribute transfer.

Original language	English
Title of host publication	Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
Publisher	IEEE Computer Society
Pages	12331-12340
Number of pages	10
ISBN (Electronic)	9781728132938
DOIs	https://doi.org/10.1109/CVPR.2019.01262
State	Published - Jun 2019
Event	32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States Duration: 16 Jun 2019 → 20 Jun 2019

Publication series

Name	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume	2019-June
ISSN (Print)	1063-6919

Conference

Conference	32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
Country/Territory	United States
City	Long Beach
Period	16/06/19 → 20/06/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Low-level Vision
Vision + Graphics

Access to Document

10.1109/CVPR.2019.01262

Cite this

Kim, S., Min, D., Jeong, S., Kim, S., Jeon, S., & Sohn, K. (2019). Semantic attribute matching networks. In Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 (pp. 12331-12340). Article 8953730 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2019-June). IEEE Computer Society. https://doi.org/10.1109/CVPR.2019.01262

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abstract = "We present semantic attribute matching networks (SAM-Net) for jointly establishing correspondences and transferring attributes across semantically similar images, which intelligently weaves the advantages of the two tasks while overcoming their limitations. SAM-Net accomplishes this through an iterative process of establishing reliable correspondences by reducing the attribute discrepancy between the images and synthesizing attribute transferred images using the learned correspondences. To learn the networks using weak supervisions in the form of image pairs, we present a semantic attribute matching loss based on the matching similarity between an attribute transferred source feature and a warped target feature. With SAM-Net, the state-of-the-art performance is attained on several benchmarks for semantic matching and attribute transfer.",

keywords = "Low-level Vision, Vision + Graphics",

author = "Seungryong Kim and Dongbo Min and Somi Jeong and Sunok Kim and Sangryul Jeon and Kwanghoon Sohn",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 ; Conference date: 16-06-2019 Through 20-06-2019",

year = "2019",

month = jun,

doi = "10.1109/CVPR.2019.01262",

language = "English",

series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",

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Kim, S, Min, D, Jeong, S, Kim, S, Jeon, S & Sohn, K 2019, Semantic attribute matching networks. in Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019., 8953730, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, IEEE Computer Society, pp. 12331-12340, 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, United States, 16/06/19. https://doi.org/10.1109/CVPR.2019.01262

Semantic attribute matching networks. / Kim, Seungryong; Min, Dongbo; Jeong, Somi et al.
Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019. IEEE Computer Society, 2019. p. 12331-12340 8953730 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2019-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - We present semantic attribute matching networks (SAM-Net) for jointly establishing correspondences and transferring attributes across semantically similar images, which intelligently weaves the advantages of the two tasks while overcoming their limitations. SAM-Net accomplishes this through an iterative process of establishing reliable correspondences by reducing the attribute discrepancy between the images and synthesizing attribute transferred images using the learned correspondences. To learn the networks using weak supervisions in the form of image pairs, we present a semantic attribute matching loss based on the matching similarity between an attribute transferred source feature and a warped target feature. With SAM-Net, the state-of-the-art performance is attained on several benchmarks for semantic matching and attribute transfer.

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Semantic attribute matching networks

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Keywords

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