Masked Event Modeling: Self-Supervised Pretraining for Event Cameras

Understand

Event cameras asynchronously capture brightness changes with low latency, high temporal resolution, and high dynamic range.

• However, annotation of event data is a costly and laborious process, which limits the use of deep learning methods for classification and other semantic tasks with the event modality.
• To reduce the dependency on labeled event data, we introduce Masked Event Modeling (MEM), a self-supervised framework for events.
• Our method pretrains a neural network on unlabeled events, which can originate from any event camera recording.