  Deep neural networks based purely on attention have been successful across
several domains, relying on minimal architectural priors from the designer. In
Human Action Recognition (HAR), attention mechanisms have been primarily
adopted on top of standard convolutional or recurrent layers, improving the
overall generalization capability. In this work, we introduce Action
Transformer (AcT), a simple, fully self-attentional architecture that
consistently outperforms more elaborated networks that mix convolutional,
recurrent and attentive layers. In order to limit computational and energy
requests, building on previous human action recognition research, the proposed
approach exploits 2D pose representations over small temporal windows,
providing a low latency solution for accurate and effective real-time
performance. Moreover, we open-source MPOSE2021, a new large-scale dataset, as
an attempt to build a formal training and evaluation benchmark for real-time,
short-time HAR. The proposed methodology was extensively tested on MPOSE2021
and compared to several state-of-the-art architectures, proving the
effectiveness of the AcT model and laying the foundations for future work on
HAR.
