  In order to deal with variant-length long videos, prior works extract
multi-modal features and fuse them to predict students' engagement intensity.
In this paper, we present a new end-to-end method Class Attention in Video
Transformer (CavT), which involves a single vector to process class embedding
and to uniformly perform end-to-end learning on variant-length long videos and
fixed-length short videos. Furthermore, to address the lack of sufficient
samples, we propose a binary-order representatives sampling method (BorS) to
add multiple video sequences of each video to augment the training set.
BorS+CavT not only achieves the state-of-the-art MSE (0.0495) on the EmotiW-EP
dataset, but also obtains the state-of-the-art MSE (0.0377) on the DAiSEE
dataset. The code and models have been made publicly available at
https://github.com/mountainai/cavt.
