Event-based Action Recognition (EAR) possesses the advantages of high-temporal resolution capturing and privacy preservation compared with traditional action recognition. Current leading EAR solutions typically follow two regimes: project unconstructed event streams into dense constructed event frames and adopt powerful frame-specific networks, or employ lightweight point-specific networks to handle sparse unconstructed event points directly. However, such two regimes are blind to a fundamental issue: failing to accommodate the unique dense temporal and sparse spatial properties of asynchronous event data. In this article, we present a synergy-aware framework, i.e., EventCrab, that adeptly integrates the "lighter" frame-specific networks for dense event frames with the "heavier" point-specific networks for sparse event points, balancing accuracy and efficiency. Furthermore, we establish a joint frame-text-point representation space to bridge distinct event frames and points. In specific, to better exploit the unique spatiotemporal relationships inherent in asynchronous event points, we devise two strategies for the "heavier" point-specific embedding: i) a Spiking-like Context Learner (SCL) that extracts contextualized event points from raw event streams. ii) an Event Point Encoder (EPE) that further explores event-point long spatiotemporal features in a Hilbert-scan way. Experiments on four datasets demonstrate the significant performance of our proposed EventCrab, particularly gaining improvements of 5.17% on SeAct and 7.01% on HARDVS.

本文研究了事件驱动动作识别（EAR）中未能有效结合异步事件数据的时间密集性与空间稀疏性的问题。我们提出了EventCrab框架，巧妙整合了面向密集事件帧的“轻量级”网络与处理稀疏事件点的“重”网络，并设计了两种策略以充分挖掘事件点的时空特性，实验表明该框架在多个数据集上显著提升性能，尤其在SeAct和HARDVS数据集上分别提高了5.17%和7.01%。

EventCrab：利用帧与点的协同作用进行事件驱动的动作识别及其扩展