  We investigate the task of identifying situations of distracted driving
through analysis of in-car videos. To tackle this challenge we introduce a
multi-task video transformer that predicts both distracted actions and driver
pose. Leveraging VideoMAEv2, a large pre-trained architecture, our approach
incorporates semantic information from human keypoint locations to enhance
action recognition and decrease computational overhead by minimizing the number
of spatio-temporal tokens. By guiding token selection with pose and class
information, we notably reduce the model's computational requirements while
preserving the baseline accuracy. Our model surpasses existing state-of-the art
results in driver action recognition while exhibiting superior efficiency
compared to current video transformer-based approaches.
