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

Temporal Convolution Networks with Positional Encoding for Evoked Expression Estimation