  This paper presents an approach for Evoked Expressions from Videos (EEV)
challenge, which aims to predict evoked facial expressions from video. We take
advantage of pre-trained models on large-scale datasets in computer vision and
audio signals to extract the deep representation of timestamps in the video. A
temporal convolution network, rather than an RNN like architecture, is used to
explore temporal relationships due to its advantage in memory consumption and
parallelism. Furthermore, to address the missing annotations of some
timestamps, positional encoding is employed to ensure continuity of input data
when discarding these timestamps during training. We achieved state-of-the-art
results on the EEV challenge with a Pearson correlation coefficient of 0.05477,
the first ranked performance in the EEV 2021 challenge.
