no code implementations • 8 Oct 2022 • Jie Liu, Jingjing Wang, Peng Zhang, Chunmao Wang, Di Xie, ShiLiang Pu
To overcome these limitations, we propose a multi-scale wavelet transformer framework for face forgery detection.
no code implementations • 1 Apr 2022 • Jingwei Yan, Jingjing Wang, Qiang Li, Chunmao Wang, ShiLiang Pu
Automatic facial action unit (AU) recognition is a challenging task due to the scarcity of manual annotations.
no code implementations • 1 Apr 2022 • Yachun Li, Ying Lian, Jingjing Wang, Yuhui Chen, Chunmao Wang, ShiLiang Pu
We thus define a new domain adaptation setting called Few-shot One-class Domain Adaptation (FODA), where adaptation only relies on a limited number of target bonafide samples.
1 code implementation • CVPR 2022 • Qiang Li, Jingjing Wang, Zhaoliang Yao, Yachun Li, Pengju Yang, Jingwei Yan, Chunmao Wang, ShiLiang Pu
In this paper, we emphatically summarize that learning an adaptive label distribution on ordinal regression tasks should follow three principles.
no code implementations • 21 Feb 2022 • Ying Bian, Peng Zhang, Jingjing Wang, Chunmao Wang, ShiLiang Pu
However, many other generalizable cues are unexplored for face anti-spoofing, which limits their performance under cross-dataset testing.
no code implementations • 30 Jul 2021 • Jingwei Yan, Jingjing Wang, Qiang Li, Chunmao Wang, ShiLiang Pu
Based on these two self-supervised auxiliary tasks, local features, mutual relation and motion cues of AUs are better captured in the backbone network with the proposed regional and temporal based auxiliary task learning (RTATL) framework.
no code implementations • 24 Feb 2021 • Jingwei Yan, Boyuan Jiang, Jingjing Wang, Qiang Li, Chunmao Wang, ShiLiang Pu
In order to incorporate the intra-level AU relation and inter-level AU regional relevance simultaneously, a multi-level AU relation graph is constructed and graph convolution is performed to further enhance AU regional features of each level.
no code implementations • 24 Feb 2021 • Jingjing Wang, Jingyi Zhang, Ying Bian, Youyi Cai, Chunmao Wang, ShiLiang Pu
In this paper, we propose a self-domain adaptation framework to leverage the unlabeled test domain data at inference.