Haibo Qiu Synface: face recognition with synthetic data this is the pytorch implementation of our iccv 2021 paper synface: face recognition with synthetic data. haibo qiu, baosheng yu, dihong gong, zhifeng li, wei liu and dacheng tao. In this paper, we address the above mentioned issues in face recognition using synthetic face images, i.e., synface. specifically, we first explore the performance gap between recent state of the art face recognition models trained with synthetic and real face images.
Github Haibo Qiu Synface Iccv 2021 Synface Face Recognition With [iccv 2021] synface: face recognition with synthetic data synface readme.md at main · haibo qiu synface. Official pytorch implementation for "synface: face recognition with synthetic data, iccv 2021" zivzone synface. My research interests lie in computer vision using deep learning, including 3d human pose estimation, face recognition and 3d point cloud semantic segmentation. This is an official pytorch implementation of "cross view fusion for 3d human pose estimation, iccv 2019". the scariest moment is always just before you start.
Haibo Qiu My research interests lie in computer vision using deep learning, including 3d human pose estimation, face recognition and 3d point cloud semantic segmentation. This is an official pytorch implementation of "cross view fusion for 3d human pose estimation, iccv 2019". the scariest moment is always just before you start. In this paper, we address the above mentioned issues in face recognition using synthetic face images, i.e., synface. specifically, we first explore the performance gap between recent state of the art face recognition models trained with synthetic and real face images. Inspired by this, we devise the synface with identity mixup (im) and domain mixup (dm) to miti gate the above performance gap, demonstrating the great potentials of synthetic data for face recognition. In this paper, we address the above mentioned issues in face recognition using synthetic face images, i.e., synface. specifically, we first explore the performance gap between recent state of the art face recognition models trained with synthetic and real face images. Pdf | on oct 1, 2021, haibo qiu and others published synface: face recognition with synthetic data | find, read and cite all the research you need on researchgate.
Haibo Qiu In this paper, we address the above mentioned issues in face recognition using synthetic face images, i.e., synface. specifically, we first explore the performance gap between recent state of the art face recognition models trained with synthetic and real face images. Inspired by this, we devise the synface with identity mixup (im) and domain mixup (dm) to miti gate the above performance gap, demonstrating the great potentials of synthetic data for face recognition. In this paper, we address the above mentioned issues in face recognition using synthetic face images, i.e., synface. specifically, we first explore the performance gap between recent state of the art face recognition models trained with synthetic and real face images. Pdf | on oct 1, 2021, haibo qiu and others published synface: face recognition with synthetic data | find, read and cite all the research you need on researchgate.
Haibo Qiu In this paper, we address the above mentioned issues in face recognition using synthetic face images, i.e., synface. specifically, we first explore the performance gap between recent state of the art face recognition models trained with synthetic and real face images. Pdf | on oct 1, 2021, haibo qiu and others published synface: face recognition with synthetic data | find, read and cite all the research you need on researchgate.
Haibo Qiu
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