  The paper addresses the problem of recognition of actions in video with low
inter-class variability such as Table Tennis strokes. Two stream, "twin"
convolutional neural networks are used with 3D convolutions both on RGB data
and optical flow. Actions are recognized by classification of temporal windows.
We introduce 3D attention modules and examine their impact on classification
efficiency. In the context of the study of sportsmen performances, a corpus of
the particular actions of table tennis strokes is considered. The use of
attention blocks in the network speeds up the training step and improves the
classification scores up to 5% with our twin model. We visualize the impact on
the obtained features and notice correlation between attention and player
movements and position. Score comparison of state-of-the-art action
classification method and proposed approach with attentional blocks is
performed on the corpus. Proposed model with attention blocks outperforms
previous model without them and our baseline.
