75 citations · 76 across the 4 of their papers we have counts for
5 papers · 1 filter
Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition
Hengshun Zhou, Debin Meng, Yuanyuan Zhang +4
The audio-video based emotion recognition aims to classify a given video into basic emotions. In this paper, we describe our approaches in EmotiW 2019, which mainly explores emotio…
Suppressing Mislabeled Data via Grouping and Self-Attention
Xiaojiang Peng, Kai Wang, Zhaoyang Zeng +3
Deep networks achieve excellent results on large-scale clean data but degrade significantly when learning from noisy labels. To suppressing the impact of mislabeled data, this pape…
Suppressing Uncertainties for Large-Scale Facial Expression Recognition
Kai Wang, Xiaojiang Peng, Jianfei Yang +2
Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, an…
Bootstrap Model Ensemble and Rank Loss for Engagement Intensity Regression
Kai Wang, Jianfei Yang, Da Guo +3
This paper presents our approach for the engagement intensity regression task of EmotiW 2019. The task is to predict the engagement intensity value of a student when he or she is w…
Frame attention networks for facial expression recognition in videos
Debin Meng, Xiaojiang Peng, Kai Wang +1
The video-based facial expression recognition aims to classify a given video into several basic emotions. How to integrate facial features of individual frames is crucial for this…