  Event cameras record sparse illumination changes with high temporal
resolution and high dynamic range. Thanks to their sparse recording and low
consumption, they are increasingly used in applications such as AR/VR and
autonomous driving. Current topperforming methods often ignore specific
event-data properties, leading to the development of generic but
computationally expensive algorithms, while event-aware methods do not perform
as well. We propose Event Transformer+, that improves our seminal work EvT with
a refined patch-based event representation and a more robust backbone to
achieve more accurate results, while still benefiting from event-data sparsity
to increase its efficiency. Additionally, we show how our system can work with
different data modalities and propose specific output heads, for event-stream
classification (i.e. action recognition) and per-pixel predictions (dense depth
estimation). Evaluation results show better performance to the state-of-the-art
while requiring minimal computation resources, both on GPU and CPU.
