no code implementations • 18 Mar 2024 • Jiahe Wang, Jiale Huang, Bingzhao Cai, Yifan Cao, Xin Yun, Shangfei Wang
Conventional approaches to facial expression recognition primarily focus on the classification of six basic facial expressions.
no code implementations • 1 Nov 2023 • Shi Yin, Shijie Huan, Shangfei Wang, Jinshui Hu, Tao Guo, Bing Yin, BaoCai Yin, Cong Liu
For temporal modeling, we propose a recurrent token mixing mechanism, an axis-landmark-positional embedding mechanism, as well as a confidence-enhanced multi-head attention mechanism to adaptively and robustly embed long-term landmark dynamics into their 1D representations; for structure modeling, we design intra-group and inter-group structure modeling mechanisms to encode the component-level as well as global-level facial structure patterns as a refinement for the 1D representations of landmarks through token communications in the spatial dimension via 1D convolutional layers.
no code implementations • 4 May 2023 • Zhou'an_Zhu, Xin Li, Jicai Pan, Yufei Xiao, Yanan Chang, Feiyi Zheng, Shangfei Wang
We also propose three labels (i. e., expression of experience, emotional reaction, and cognitive reaction) to describe the degree of empathy between counselors and their clients.
no code implementations • 16 Mar 2023 • Shangfei Wang, Jiaqiang Wu, Feiyi Zheng, Xin Li, XueWei Li, Suwen Wang, Yi Wu, Yanan Chang, Xiangyu Miao
In this paper, 1. better features are extracted with the SOTA pretrained models.
no code implementations • 16 Mar 2023 • Shangfei Wang, Yanan Chang, Yi Wu, Xiangyu Miao, Jiaqiang Wu, Zhouan Zhu, Jiahe Wang, Yufei Xiao
Facial affective behavior analysis is important for human-computer interaction.
no code implementations • ACM MM22 2022 • Jicai Pan, Shangfei Wang, Lin Fang
These self-supervised pre-training tasks prompt the fusion module to perform representation learning on segments including TSC, thus capturing more temporal affective patterns.
Ranked #1 on Video Emotion Recognition on Ekman6 (using extra training data)
no code implementations • 20 Jul 2022 • Yanan Chang, Yi Wu, Xiangyu Miao, Jiahe Wang, Shangfei Wang
The 4th competition on affective behavior analysis in the wild (ABAW) provided images with valence/arousal, expression and action unit labels.
no code implementations • 20 Jul 2022 • Xiangyu Miao, Jiahe Wang, Yanan Chang, Yi Wu, Shangfei Wang
Learning from synthetic images plays an important role in facial expression recognition task due to the difficulties of labeling the real images, and it is challenging because of the gap between the synthetic images and real images.
Facial Expression Recognition Facial Expression Recognition (FER)
no code implementations • 25 Mar 2022 • Shangfei Wang, Yanan Chang, Jiahe Wang
Then we fine-tune the network for facial action unit recognition.
no code implementations • CVPR 2022 • Yanan Chang, Shangfei Wang
To remedy this, we utilize AU labeling rules defined by the Facial Action Coding System (FACS) to design a novel knowledge-driven self-supervised representation learning framework for AU recognition.
no code implementations • 4 Jun 2021 • Shangfei Wang, Yanan Chang, Guozhu Peng, Bowen Pan
Specifically, the proposed deep semi-supervised AU recognition approach consists of a deep recognition network and a discriminator D. The deep recognition network R learns facial representations from large-scale facial images and AU classifiers from limited ground truth AU labels.
no code implementations • 12 Jul 2020 • Guang Liang, Shangfei Wang, Can Wang
The first aims to learn pose- and expression-related feature representations in the source domain and adapt both feature distributions to that of the target domain by imposing adversarial learning.
no code implementations • 5 Apr 2020 • Shi Yin, Shangfei Wang, Xiaoping Chen, Enhong Chen
These 1D heatmaps reduce spatial complexity significantly compared to current heatmap regression methods, which use 2D heatmaps to represent the joint distributions of x and y coordinates.
no code implementations • 18 Nov 2019 • Shangfei Wang, Shi Yin, Longfei Hao, Guang Liang
Through multi-task learning mechanism, the recognition network explores the dependencies among multiple face analysis tasks, such as facial landmark localization, head pose estimation, gender recognition and face attribute estimation from image representation-level.
no code implementations • 3 Oct 2019 • Sicheng Zhao, Shangfei Wang, Mohammad Soleymani, Dhiraj Joshi, Qiang Ji
Affective computing (AC) of these data can help to understand human behaviors and enable wide applications.
no code implementations • 28 Aug 2018 • Shi Yin, Yi Zhou, Chenguang Li, Shangfei Wang, Jianmin Ji, Xiaoping Chen, Ruili Wang
We propose KDSL, a new word sense disambiguation (WSD) framework that utilizes knowledge to automatically generate sense-labeled data for supervised learning.
no code implementations • CVPR 2018 • Guozhu Peng, Shangfei Wang
Then we propose a weakly supervised AU recognition method via an adversarial process, in which we simultaneously train two models: a recognition model R, which learns AU classifiers, and a discrimination model D, which estimates the probability that AU labels generated from domain knowledge rather than the recognized AU labels from R. The training procedure for R maximizes the probability of D making a mistake.
no code implementations • ICCV 2017 • Shan Wu, Shangfei Wang, Bowen Pan, Qiang Ji
To address this, we propose a deep facial action unit recognition approach learning from partially AU-labeled data.
no code implementations • ICCV 2017 • Quan Gan, Shangfei Wang, Longfei Hao, Qiang Ji
After that, a joint representation is extracted from the top layers of the two deep networks, and thus captures the high order dependencies between visual modality and audio modality.
no code implementations • 29 Mar 2017 • Shiyu Chen, Shangfei Wang, Tanfang Chen, Xiaoxiao Shi
In this paper, we propose a novel approach for learning multi-label classifiers with the help of privileged information.
no code implementations • CVPR 2016 • Rui Zhao, Quan Gan, Shangfei Wang, Qiang Ji
In fully supervised case, all the frames are provided with intensity annotations.
no code implementations • CVPR 2013 • Ziheng Wang, Shangfei Wang, Qiang Ji
Spatial-temporal relations among facial muscles carry crucial information about facial expressions yet have not been thoroughly exploited.
Facial Expression Recognition Facial Expression Recognition (FER)