  In this work we present a new efficient approach to Human Action Recognition
called Video Transformer Network (VTN). It leverages the latest advances in
Computer Vision and Natural Language Processing and applies them to video
understanding. The proposed method allows us to create lightweight CNN models
that achieve high accuracy and real-time speed using just an RGB mono camera
and general purpose CPU. Furthermore, we explain how to improve accuracy by
distilling from multiple models with different modalities into a single model.
We conduct a comparison with state-of-the-art methods and show that our
approach performs on par with most of them on famous Action Recognition
datasets. We benchmark the inference time of the models using the modern
inference framework and argue that our approach compares favorably with other
methods in terms of speed/accuracy trade-off, running at 56 FPS on CPU. The
models and the training code are available.
