Audio-visual emotion recognition using deep transfer learning and multiple temporal models-Reference-Cited by-同舟云学术

Audio-visual emotion recognition using deep transfer learning and multiple temporal models

Published:2017-11-03 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 19th ACM International Conference on Multimodal Interaction
language:
Short-container-title:

Author:

Ouyang Xi¹,Kawaai Shigenori¹,Goh Ester Gue Hua¹,Shen Shengmei¹,Ding Wan²,Ming Huaiping³,Huang Dong-Yan³

Affiliation:

1. Panasonic R&D Center, Singapore

2. Central China Normal University, China

3. Institute for Infocomm Research at A*STAR, Singapore

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3136755.3143012

Reference27 articles.

1. From individual to group-level emotion recognition: EmotiW 5.0

2. From generic to specific deep representations for visual recognition

3. Emotion recognition in the wild from videos using images

4. R. Fergus L. Torresani D. Tran L. Bourdev and M. Paluri. 2015. Learning Spatiotemporal Features with 3D Convolutional Networks. ICCV (2015). R. Fergus L. Torresani D. Tran L. Bourdev and M. Paluri. 2015. Learning Spatiotemporal Features with 3D Convolutional Networks. ICCV (2015).

5. ICMI’17 November 13–17 2017 Glasgow UK X. Ouyang et al. ICMI’17 November 13–17 2017 Glasgow UK X. Ouyang et al.

Cited by 37 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A joint local spatial and global temporal CNN-Transformer for dynamic facial expression recognition;Applied Soft Computing;2024-08

2. Improved MobileNetv3 lightweight dynamic expression recognition algorithm for classroom scenarios;2024-06-03

3. A Deep Learning Framework for Monitoring Audience Engagement in Online Video Events;International Journal of Computational Intelligence Systems;2024-05-21

4. Temporal signed gestures segmentation in an image sequence using deep reinforcement learning;Engineering Applications of Artificial Intelligence;2024-05

5. Deep temporal clustering features for speech emotion recognition;Speech Communication;2024-02