  In the latest social networks, more and more people prefer to express their
emotions in videos through text, speech, and rich facial expressions.
Multimodal video emotion analysis techniques can help understand users' inner
world automatically based on human expressions and gestures in images, tones in
voices, and recognized natural language. However, in the existing research, the
acoustic modality has long been in a marginal position as compared to visual
and textual modalities. That is, it tends to be more difficult to improve the
contribution of the acoustic modality for the whole multimodal emotion
recognition task. Besides, although better performance can be obtained by
introducing common deep learning methods, the complex structures of these
training models always result in low inference efficiency, especially when
exposed to high-resolution and long-length videos. Moreover, the lack of a
fully end-to-end multimodal video emotion recognition system hinders its
application. In this paper, we designed a fully multimodal video-to-emotion
system (named FV2ES) for fast yet effective recognition inference, whose
benefits are threefold: (1) The adoption of the hierarchical attention method
upon the sound spectra breaks through the limited contribution of the acoustic
modality and outperforms the existing models' performance on both IEMOCAP and
CMU-MOSEI datasets; (2) the introduction of the idea of multi-scale for visual
extraction while single-branch for inference brings higher efficiency and
maintains the prediction accuracy at the same time; (3) the further integration
of data pre-processing into the aligned multimodal learning model allows the
significant reduction of computational costs and storage space.
