2 code implementations • 3 Mar 2022 • Binjie Zhang, Yixiao Ge, Yantao Shen, Shupeng Su, Fanzi Wu, Chun Yuan, Xuyuan Xu, Yexin Wang, Ying Shan
The task of backward-compatible representation learning is therefore introduced to support backfill-free model upgrades, where the new query features are interoperable with the old gallery features.
no code implementations • 1 Jan 2021 • Yantao Shen, Fanzi Wu, Ying Shan
In this work, we introduce an approach for feature compatible learning without inheriting old classifier and training data, i. e., Non-Inherent Feature Compatible Learning.
1 code implementation • 25 Oct 2019 • Yajing Chen, Fanzi Wu, Zeyu Wang, Yibing Song, Yonggen Ling, Linchao Bao
The displacement map and the coarse model are used to render a final detailed face, which again can be compared with the original input image to serve as a photometric loss for the second stage.
1 code implementation • CVPR 2019 • Fanzi Wu, Linchao Bao, Yajing Chen, Yonggen Ling, Yibing Song, Songnan Li, King Ngi Ngan, Wei Liu
The main ingredient of the view alignment loss is a differentiable dense optical flow estimator that can backpropagate the alignment errors between an input view and a synthetic rendering from another input view, which is projected to the target view through the 3D shape to be inferred.
no code implementations • 10 Dec 2017 • Fanzi Wu, Songnan Li, Tianhao Zhao, King Ngi Ngan, Lv Sheng
2D landmarks are detected and used to initialize the 3D shape and mapping matrices.