  Despite recent advances in computer vision based on various convolutional
architectures, video understanding remains an important challenge. In this
work, we present and discuss a top solution for the large-scale video
classification (labeling) problem introduced as a Kaggle competition based on
the YouTube-8M dataset. We show and compare different approaches to
preprocessing, data augmentation, model architectures, and model combination.
Our final model is based on a large ensemble of video- and frame-level models
but fits into rather limiting hardware constraints. We apply an approach based
on knowledge distillation to deal with noisy labels in the original dataset and
the recently developed mixup technique to improve the basic models.
