Dual-level attention-aware network for temporal emotion segmentation

Bo Sun; Meng Guo; Siming Cao; Jun He; Lejun Yu

doi:10.1117/1.JEI.27.3.033012

14 May 2018 Dual-level attention-aware network for temporal emotion segmentation

Bo Sun, Meng Guo, Siming Cao, Jun He, Lejun Yu

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 3, 033012 (May 2018). https://doi.org/10.1117/1.JEI.27.3.033012

Abstract

Human emotions are known to always have four phases in the temporal domain: neutral, onset, apex, and offset. This has been demonstrated to be of great benefit for emotion recognition. Therefore, temporal segmentation has attracted considerable research interest. Although state-of-the-art techniques use recurrent neural networks to highly increase the performance, they ignore the relevance of each frame (time step) of a video, and they do not consider the changing contribution of different features when fusing them. We propose a framework called dual-level attention-aware bidirectional grated recurrent unit, which integrates ideas from attention models to discover the most important frames and features for improving temporal segmentation. Specifically, it applies attention mechanisms at two levels: frame and feature. A significant advantage is that the two-level attention weights provide a meaningful value to depict the importance of each frame and feature. The experiments demonstrated that the proposed framework outperforms state-of-the-art methods.

Citation Download Citation

Bo Sun, Meng Guo, Siming Cao, Jun He, and Lejun Yu "Dual-level attention-aware network for temporal emotion segmentation," Journal of Electronic Imaging 27(3), 033012 (14 May 2018). https://doi.org/10.1117/1.JEI.27.3.033012

Received: 8 December 2017; Accepted: 24 April 2018; Published: 14 May 2018

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