Less is More: Sparse Sampling for Dense Reaction Predictions
This work addresses the need for video creators and streaming platforms to analyze performance and improve user experience, but it is incremental as it applies existing methods to a specific challenge.
The authors tackled the problem of predicting viewer emotional responses from videos by using a GRU-based model with sparse sampling at 1Hz and linear interpolation for dense predictions, achieving a Pearson's correlation score of 0.04430 on a private test set.
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.