Psychological research results have confirmed that people can have different
emotional reactions to different visual stimuli. Several papers have been
published on the problem of visual emotion analysis. In particular, attempts
have been made to analyze and predict people's emotional reaction towards
images. To this end, different kinds of hand-tuned features are proposed. The
results reported on several carefully selected and labeled small image data
sets have confirmed the promise of such features. While the recent successes of
many computer vision related tasks are due to the adoption of Convolutional
Neural Networks (CNNs), visual emotion analysis has not achieved the same level
of success. This may be primarily due to the unavailability of confidently
labeled and relatively large image data sets for visual emotion analysis. In
this work, we introduce a new data set, which started from 3+ million weakly
labeled images of different emotions and ended up 30 times as large as the
current largest publicly available visual emotion data set. We hope that this
data set encourages further research on visual emotion analysis. We also
perform extensive benchmarking analyses on this large data set using the state
of the art methods including CNNs.

本文介绍了一个新的图像数据集，旨在鼓励进一步研究视觉情感分析。 在此数据集上进行了广泛的基准测试分析，包括使用卷积神经网络等最新方法，证实了手工调整特征的有效性以及由于缺乏数据而导致的视觉情感分析发展滞后的挑战。