Abstract
Event-based cameras capture visual information as asynchronous streams of per-pixel brightness changes, generating sparse, temporally precise data. Compared to conventional frame-based sensors, they offer significant advantages in capturing high-speed dynamics while consuming substantially less power. Predicting future event representations from past observations is an important problem, enabling downstream tasks such as future semantic segmentation or object tracking without requiring access to future sensor measurements. While recent state-of-the-art approaches achieve strong performance, they often rely on computationally heavy backbones and, in some cases, large-scale pretraining, limiting their applicability in resource-constrained scenarios. In this work, we introduce E-TIDE, a lightweight, end-to-end trainable architecture for event-tensor prediction that is designed to operate efficiently without large-scale pretraining. Our approach employs the TIDE module (Temporal Interaction for Dynamic Events), motivated by efficient spatiotemporal interaction design for sparse event tensors, to capture temporal dependencies via large-kernel mixing and activity-aware gating while maintaining low computational complexity. Experiments on standard event-based datasets demonstrate that our method achieves competitive performance with significantly reduced model size and training requirements, making it well-suited for real-time deployment under tight latency and memory budgets.
Abstract (translated)
事件相机通过异步流的形式捕获每个像素亮度变化，生成稀疏且时间精确的数据。与传统的帧式传感器相比，它们在捕捉高速动态场景时具有显著优势，同时功耗大幅降低。基于历史观测预测未来事件表示是一个重要问题，它使得未来语义分割或目标跟踪等下游任务无需访问未来传感器测量值即可进行。尽管近期最先进的方法取得了优异性能，但它们通常依赖计算量庞大的骨干网络，部分还需大规模预训练，这限制了其在资源受限场景中的应用。本研究提出E-TIDE，一种轻量级、端到端可训练的事件张量预测架构，专为无需大规模预训练的高效运行而设计。该方法采用TIDE模块（时间交互动态事件模块），其动机在于针对稀疏事件张量设计高效时空交互，通过大核混合和感知活动门控来捕获时间依赖关系，同时保持低计算复杂度。在标准事件数据集上的实验表明，我们的方法在模型规模和训练需求显著减少的情况下仍能达到具有竞争力的性能，非常适合在严格延迟和内存预算下进行实时部署。
URL
https://arxiv.org/abs/2603.27757
