no code implementations • 10 Mar 2023 • Juan Song, Zhilei Liu
Facial action unit (AU) detection is a challenging task due to the scarcity of manual annotations.
no code implementations • 12 Oct 2022 • Chenggong Zhang, Zhilei Liu
In this paper, we propose a novel recurrent convolutional network based framework for face super-resolution, which progressively introduces both global shape and local texture information.
no code implementations • 18 May 2022 • Jiyuan Cao, Zhilei Liu, Yong Zhang
Ablation study and visualization show that our MARL can eliminate identity-caused differences, thus obtaining a robust and generalized AU discriminative embedding representation.
no code implementations • 27 Apr 2022 • Sen Chen, Zhilei Liu, Jiaxing Liu, Longbiao Wang
We utilize pre-trained AU classifier to ensure that the generated images contain correct AU information.
no code implementations • 19 Oct 2021 • Sen Chen, Zhilei Liu, Jiaxing Liu, Zhengxiang Yan, Longbiao Wang
Quantitative and qualitative experiments demonstrate that our method outperforms existing methods in both image quality and lip-sync accuracy.
no code implementations • 9 Jul 2021 • Chenggong Zhang, Juan Song, Qingyang Zhang, Weilong Dong, Ruomeng Ding, Zhilei Liu
This paper describes an approach to the facial action unit (AU) detection.
no code implementations • 19 Oct 2020 • Ruixin Xiao, Zhilei Liu, Baoyuan Wu
With the supervision from source domain only in class-level, existing unsupervised domain adaptation (UDA) methods mainly learn the domain-invariant representations from a shared feature extractor, which causes the source-bias problem.
1 code implementation • 18 Mar 2020 • Zhiwen Shao, Zhilei Liu, Jianfei Cai, Lizhuang Ma
Moreover, to extract precise local features, we propose an adaptive attention learning module to refine the attention map of each AU adaptively.
no code implementations • 29 Feb 2020 • Zhilei Liu, Yunpeng Wu, Le Li, Cuicui Zhang, Baoyuan Wu
This paper proposes a multi-scale feature graph generative adversarial network (MFG-GAN) to implement the face restoration of images in which both degradation modes coexist, and also to repair images with a single type of degradation.
no code implementations • 26 Feb 2020 • Yong Zhang, Le Li, Zhilei Liu, Baoyuan Wu, Yanbo Fan, Zhifeng Li
Most of the existing methods train models for one-versus-one kin relation, which only consider one parent face and one child face by directly using an auto-encoder without any explicit control over the resemblance of the synthesized face to the parent face.
no code implementations • 23 Oct 2019 • Zhilei Liu, Le Li, Yunpeng Wu, Cuicui Zhang
Facial expression analysis in the wild is challenging when the facial image is with low resolution or partial occlusion.
no code implementations • 23 Oct 2019 • Zhilei Liu, Diyi Liu, Yunpeng Wu
Facial expression synthesis or editing has recently received increasing attention in the field of affective computing and facial expression modeling.
no code implementations • 23 Oct 2019 • Zhilei Liu, Jiahui Dong, Cuicui Zhang, Longbiao Wang, Jianwu Dang
Most existing AU detection works considering AU relationships are relying on probabilistic graphical models with manually extracted features.
no code implementations • 10 Aug 2018 • Zhiwen Shao, Zhilei Liu, Jianfei Cai, Yunsheng Wu, Lizhuang Ma
By finding the region of interest of each AU with the attention mechanism, AU-related local features can be captured.
no code implementations • 21 Mar 2018 • Haotian Guan, Zhilei Liu, Longbiao Wang, Jianwu Dang, Ruiguo Yu
Recently, increasing attention has been directed to the study of the speech emotion recognition, in which global acoustic features of an utterance are mostly used to eliminate the content differences.
1 code implementation • ECCV 2018 • Zhiwen Shao, Zhilei Liu, Jianfei Cai, Lizhuang Ma
Facial action unit (AU) detection and face alignment are two highly correlated tasks since facial landmarks can provide precise AU locations to facilitate the extraction of meaningful local features for AU detection.
Ranked #3 on Facial Action Unit Detection on DISFA
no code implementations • 21 Feb 2018 • Zhilei Liu, Guoxian Song, Jianfei Cai, Tat-Jen Cham, Juyong Zhang
Employing deep learning-based approaches for fine-grained facial expression analysis, such as those involving the estimation of Action Unit (AU) intensities, is difficult due to the lack of a large-scale dataset of real faces with sufficiently diverse AU labels for training.
no code implementations • 20 Sep 2017 • Yan Ji, Licheng Liu, Hongcui Wang, Zhilei Liu, Zhibin Niu, Bruce Denby
The 2010 Silent Speech Challenge benchmark is updated with new results obtained in a Deep Learning strategy, using the same input features and decoding strategy as in the original article.