TY - GEN
T1 - Semantic attribute matching networks
AU - Kim, Seungryong
AU - Min, Dongbo
AU - Jeong, Somi
AU - Kim, Sunok
AU - Jeon, Sangryul
AU - Sohn, Kwanghoon
N1 - Funding Information:
This research was supported by R&D program for Advanced Integrated-intelligenceforIdentification(AIID)throughtheNationalRe-searchFoundationofKOREA (NRF)fundedbyMinistryofScienceand ICT(NRF-2018M3E3A1057289). ∗Correspondingauthor
Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - 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.
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.
KW - Low-level Vision
KW - Vision + Graphics
UR - http://www.scopus.com/inward/record.url?scp=85078720831&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2019.01262
DO - 10.1109/CVPR.2019.01262
M3 - Conference contribution
AN - SCOPUS:85078720831
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 12331
EP - 12340
BT - Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
PB - IEEE Computer Society
T2 - 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019
Y2 - 16 June 2019 through 20 June 2019
ER -