  Nowadays, short videos (SVs) are essential to information acquisition and
sharing in our life. The prevailing use of SVs to spread emotions leads to the
necessity of emotion recognition in SVs. Considering the lack of SVs emotion
data, we introduce a large-scale dataset named eMotions, comprising 27,996
videos. Meanwhile, we alleviate the impact of subjectivities on labeling
quality by emphasizing better personnel allocations and multi-stage
annotations. In addition, we provide the category-balanced and test-oriented
variants through targeted data sampling. Some commonly used videos (e.g.,
facial expressions and postures) have been well studied. However, it is still
challenging to understand the emotions in SVs. Since the enhanced content
diversity brings more distinct semantic gaps and difficulties in learning
emotion-related features, and there exists information gaps caused by the
emotion incompleteness under the prevalently audio-visual co-expressions. To
tackle these problems, we present an end-to-end baseline method AV-CPNet that
employs the video transformer to better learn semantically relevant
representations. We further design the two-stage cross-modal fusion module to
complementarily model the correlations of audio-visual features. The EP-CE
Loss, incorporating three emotion polarities, is then applied to guide model
optimization. Extensive experimental results on nine datasets verify the
effectiveness of AV-CPNet. Datasets and code will be open on
https://github.com/XuecWu/eMotions.
