EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes
Jingyuan Yang, Qirui Huang, Tingting Ding, Dani Lischinski, Daniel, Cohen-Or, Hui Huang

TL;DR
EmoSet is a large-scale, richly annotated visual emotion dataset designed to advance research in affective computing by providing diverse, balanced images with detailed emotion attributes and annotations.
Contribution
The paper introduces EmoSet, the first large-scale visual emotion dataset with rich attribute annotations, surpassing existing datasets in size, diversity, and annotation depth.
Findings
EmoSet contains 3.3 million images, with 118,102 human-labeled images.
The dataset includes diverse images from social networks and art, balanced across emotion categories.
Emotion attributes such as brightness, colorfulness, and scene type are validated for their relevance to visual emotions.
Abstract
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…
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Taxonomy
TopicsAdvanced Computing and Algorithms · Sentiment Analysis and Opinion Mining · Emotion and Mood Recognition
