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.
|Title of host publication||Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019|
|Publisher||IEEE Computer Society|
|Number of pages||10|
|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
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Conference||32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019|
|Period||16/06/19 → 20/06/19|
Bibliographical noteFunding Information:
This research was supported by R&D program for Advanced Integrated-intelligenceforIdentification(AIID)throughtheNationalRe-searchFoundationofKOREA (NRF)fundedbyMinistryofScienceand ICT(NRF-2018M3E3A1057289). ∗Correspondingauthor
© 2019 IEEE.
- Low-level Vision
- Vision + Graphics