An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos

Understand

Emotion recognition in user-generated videos plays an important role in human-centered computing.

• Existing methods mainly employ traditional two-stage shallow pipeline, i.e.
• extracting visual and/or audio features and training classifiers.
• In this paper, we propose to recognize video emotions in an end-to-end manner based on convolutional neural networks (CNNs).