  We propose a concise representation of videos that encode perceptually
meaningful features into graphs. With this representation, we aim to leverage
the large amount of redundancies in videos and save computations. First, we
construct superpixel-based graph representations of videos by considering
superpixels as graph nodes and create spatial and temporal connections between
adjacent superpixels. Then, we leverage Graph Convolutional Networks to process
this representation and predict the desired output. As a result, we are able to
train models with much fewer parameters, which translates into short training
periods and a reduction in computation resource requirements. A comprehensive
experimental study on the publicly available datasets Kinetics-400 and Charades
shows that the proposed method is highly cost-effective and uses limited
commodity hardware during training and inference. It reduces the computational
requirements 10-fold while achieving results that are comparable to
state-of-the-art methods. We believe that the proposed approach is a promising
direction that could open the door to solving video understanding more
efficiently and enable more resource limited users to thrive in this research
field.
