  Videos can evoke a range of affective responses in viewers. The ability to
predict evoked affect from a video, before viewers watch the video, can help in
content creation and video recommendation. We introduce the Evoked Expressions
from Videos (EEV) dataset, a large-scale dataset for studying viewer responses
to videos. Each video is annotated at 6 Hz with 15 continuous evoked expression
labels, corresponding to the facial expression of viewers who reacted to the
video. We use an expression recognition model within our data collection
framework to achieve scalability. In total, there are 36.7 million annotations
of viewer facial reactions to 23,574 videos (1,700 hours). We use a publicly
available video corpus to obtain a diverse set of video content. We establish
baseline performance on the EEV dataset using an existing multimodal recurrent
model. Transfer learning experiments show an improvement in performance on the
LIRIS-ACCEDE video dataset when pre-trained on EEV. We hope that the size and
diversity of the EEV dataset will encourage further explorations in video
understanding and affective computing. A subset of EEV is released at
https://github.com/google-research-datasets/eev.
