An End-to-End Visual-Audio Attention Network for Emotion Recognition in User-Generated Videos
This work addresses emotion recognition for human-centered computing, representing an incremental improvement over existing two-stage methods.
The paper tackled emotion recognition in user-generated videos by proposing an end-to-end Visual-Audio Attention Network (VAANet) that integrates multiple attention mechanisms, achieving state-of-the-art performance on VideoEmotion-8 and Ekman-6 datasets.
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). Specifically, we develop a deep Visual-Audio Attention Network (VAANet), a novel architecture that integrates spatial, channel-wise, and temporal attentions into a visual 3D CNN and temporal attentions into an audio 2D CNN. Further, we design a special classification loss, i.e. polarity-consistent cross-entropy loss, based on the polarity-emotion hierarchy constraint to guide the attention generation. Extensive experiments conducted on the challenging VideoEmotion-8 and Ekman-6 datasets demonstrate that the proposed VAANet outperforms the state-of-the-art approaches for video emotion recognition. Our source code is released at: https://github.com/maysonma/VAANet.