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.

翻译：动态视觉传感器（DVS）捕获的事件数据提供了一种区别于传统视频采集的视觉处理新途径，其在动态实时场景中展现出高效性。尽管具备高时间分辨率与低能耗等优势，事件数据的应用仍受限于数据集规模与多样性的不足。为此，我们提出了EventZoom——一种专为事件数据设计的数据增强策略。该方法采用渐进式时间策略，通过智能融合时间与空间维度，在保持数据真实性的同时有效提升其多样性与复杂性。此方法旨在改善模型训练数据的质量，并增强算法处理复杂动态场景的适应性与鲁棒性。我们在多种监督学习框架（包括全监督、半监督及无监督学习）中对EventZoom进行了实验验证。结果表明，EventZoom在不同学习场景中均稳定优于其他数据增强方法，证实了其作为一种高效事件数据增强工具的有效性与普适性。