Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition-Reference-Cited by-同舟云学术

Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition

Published:2019-10-14 Issue: Volume: Page:
ISSN:
Container-title:2019 International Conference on Multimodal Interaction
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Author:

Zhou Hengshun¹,Meng Debin²,Zhang Yuanyuan¹,Peng Xiaojiang²,Du Jun¹,Wang Kai²,Qiao Yu²

Affiliation:

1. National Engineering Laboratory for Speech and Language Information Processing (NEL-SLIP), University of Science and Technology of China, P.R.China

2. ShenZhen Key Lab of Computer Vision and Pattern Recognition, SIAT-SenseTime Joint Lab, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3340555.3355713

Reference32 articles.

1. Sarah Adel Bargal Emad Barsoum Cristian Canton Ferrer and Cha Zhang. 2016. Emotion recognition in the wild from videos using images. In ACM ICMI. Sarah Adel Bargal Emad Barsoum Cristian Canton Ferrer and Cha Zhang. 2016. Emotion recognition in the wild from videos using images. In ACM ICMI.

2. Emad Barsoum Cha Zhang Cristian Canton Ferrer and Zhengyou Zhang. 2016. Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution. In ACM ICMI. Emad Barsoum Cha Zhang Cristian Canton Ferrer and Zhengyou Zhang. 2016. Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution. In ACM ICMI.

3. 3-D Convolutional Recurrent Neural Networks With Attention Model for Speech Emotion Recognition

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