  This work focuses on the apparent emotional reaction recognition (AERR) from
the video-only input, conducted in a self-supervised fashion. The network is
first pre-trained on different self-supervised pretext tasks and later
fine-tuned on the downstream target task. Self-supervised learning facilitates
the use of pre-trained architectures and larger datasets that might be deemed
unfit for the target task and yet might be useful to learn informative
representations and hence provide useful initializations for further
fine-tuning on smaller more suitable data. Our presented contribution is
two-fold: (1) an analysis of different state-of-the-art (SOTA) pretext tasks
for the video-only apparent emotional reaction recognition architecture, and
(2) an analysis of various combinations of the regression and classification
losses that are likely to improve the performance further. Together these two
contributions result in the current state-of-the-art performance for the
video-only spontaneous apparent emotional reaction recognition with continuous
annotations.
