  Event cameras are sensors of great interest for many applications that run in
low-resource and challenging environments. They log sparse illumination changes
with high temporal resolution and high dynamic range, while they present
minimal power consumption. However, top-performing methods often ignore
specific event-data properties, leading to the development of generic but
computationally expensive algorithms. Efforts toward efficient solutions
usually do not achieve top-accuracy results for complex tasks. This work
proposes a novel framework, Event Transformer (EvT), that effectively takes
advantage of event-data properties to be highly efficient and accurate. We
introduce a new patch-based event representation and a compact transformer-like
architecture to process it. EvT is evaluated on different event-based
benchmarks for action and gesture recognition. Evaluation results show better
or comparable accuracy to the state-of-the-art while requiring significantly
less computation resources, which makes EvT able to work with minimal latency
both on GPU and CPU.
