  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.
