  Event data captured by Dynamic Vision Sensors (DVS) offers a unique approach
to visual processing that differs from traditional video capture, showcasing
its efficiency in dynamic and real-time scenarios. Despite advantages such as
high temporal resolution and low energy consumption, the application of event
data faces challenges due to limited dataset size and diversity. To address
this, we developed EventZoom -- a data augmentation strategy specifically
designed for event data. EventZoom employs a progressive temporal strategy that
intelligently blends time and space to enhance the diversity and complexity of
the data while maintaining its authenticity. This method aims to improve the
quality of data for model training and enhance the adaptability and robustness
of algorithms in handling complex dynamic scenes. We have experimentally
validated EventZoom across various supervised learning frameworks, including
supervised, semi-supervised, and unsupervised learning. Our results demonstrate
that EventZoom consistently outperforms other data augmentation methods,
confirming its effectiveness and applicability as a powerful event-based data
augmentation tool in diverse learning settings.
