Visual Emotion Analysis (VEA) aims at predicting people's emotional responses
to visual stimuli. This is a promising, yet challenging, task in affective
computing, which has drawn increasing attention in recent years. Most of the
existing work in this area focuses on feature design, while little attention
has been paid to dataset construction. In this work, we introduce EmoSet, the
first large-scale visual emotion dataset annotated with rich attributes, which
is superior to existing datasets in four aspects: scale, annotation richness,
diversity, and data balance. EmoSet comprises 3.3 million images in total, with
118,102 of these images carefully labeled by human annotators, making it five
times larger than the largest existing dataset. EmoSet includes images from
social networks, as well as artistic images, and it is well balanced between
different emotion categories. Motivated by psychological studies, in addition
to emotion category, each image is also annotated with a set of describable
emotion attributes: brightness, colorfulness, scene type, object class, facial
expression, and human action, which can help understand visual emotions in a
precise and interpretable way. The relevance of these emotion attributes is
validated by analyzing the correlations between them and visual emotion, as
well as by designing an attribute module to help visual emotion recognition. We
believe EmoSet will bring some key insights and encourage further research in
visual emotion analysis and understanding. The data and code will be released
after the publication of this work.

本文引入了 EmoSet 数据集，这是第一个使用丰富属性注释的大型视觉情感数据集。它包括社交网络、艺术形象等多种图像，并平衡地反映了不同情感类别。除情感类别外，每幅图像还用亮度、色彩、场景类型、物体类别、面部表情和人类动作进行了注释，出版后将发布数据和代码。