  With the fast development of artificial intelligence and short videos,
emotion recognition in short videos has become one of the most important
research topics in human-computer interaction. At present, most emotion
recognition methods still stay in a single modality. However, in daily life,
human beings will usually disguise their real emotions, which leads to the
problem that the accuracy of single modal emotion recognition is relatively
terrible. Moreover, it is not easy to distinguish similar emotions. Therefore,
we propose a new approach denoted as ICANet to achieve multimodal short video
emotion recognition by employing three different modalities of audio, video and
optical flow, making up for the lack of a single modality and then improving
the accuracy of emotion recognition in short videos. ICANet has a better
accuracy of 80.77% on the IEMOCAP benchmark, exceeding the SOTA methods by
15.89%.
