Multi-Granularity Network with Modal Attention for Dense Affective Understanding

• URL: http://arxiv.org/abs/2106.09964v1
• Date: Fri, 18 Jun 2021 07:37:06 GMT
• Title: Multi-Granularity Network with Modal Attention for Dense Affective Understanding
• Authors: Baoming Yan, Lin Wang, Ke Gao, Bo Gao, Xiao Liu, Chao Ban, Jiang Yang, Xiaobo Li
• Abstract summary: In the recent EEV challenge, a dense affective understanding task is proposed and requires frame-level affective prediction. We propose a multi-granularity network with modal attention (MGN-MA), which employs multi-granularity features for better description of the target frame. The proposed method achieves the correlation score of 0.02292 in the EEV challenge.
• Score: 11.076925361793556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video affective understanding, which aims to predict the evoked expressions by the video content, is desired for video creation and recommendation. In the recent EEV challenge, a dense affective understanding task is proposed and requires frame-level affective prediction. In this paper, we propose a multi-granularity network with modal attention (MGN-MA), which employs multi-granularity features for better description of the target frame. Specifically, the multi-granularity features could be divided into frame-level, clips-level and video-level features, which corresponds to visual-salient content, semantic-context and video theme information. Then the modal attention fusion module is designed to fuse the multi-granularity features and emphasize more affection-relevant modals. Finally, the fused feature is fed into a Mixtures Of Experts (MOE) classifier to predict the expressions. Further employing model-ensemble post-processing, the proposed method achieves the correlation score of 0.02292 in the EEV challenge.

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