  Obtaining viewer responses from videos can be useful for creators and
streaming platforms to analyze the video performance and improve the future
user experience. In this report, we present our method for 2021 Evoked
Expression from Videos Challenge. In particular, our model utilizes both audio
and image modalities as inputs to predict emotion changes of viewers. To model
long-range emotion changes, we use a GRU-based model to predict one sparse
signal with 1Hz. We observe that the emotion changes are smooth. Therefore, the
final dense prediction is obtained via linear interpolating the signal, which
is robust to the prediction fluctuation. Albeit simple, the proposed method has
achieved pearson's correlation score of 0.04430 on the final private test set.
