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.

EventZoom 是一种针对事件数据的数据增强策略，利用渐进式时间策略智能地融合时间和空间，增强数据的多样性和复杂性，从而提高模型训练的数据质量，并增强算法在处理复杂动态场景中的适应性和稳健性。通过在各种监督学习框架中实验验证，结果表明 EventZoom 始终优于其他数据增强方法，在不同的学习环境中作为强大的事件数据增强工具具有效果和适用性。