The best performing learning algorithms devised for event cameras work by
first converting events into dense representations that are then processed
using standard CNNs. However, these steps discard both the sparsity and high
temporal resolution of events, leading to high computational burden and
latency. For this reason, recent works have adopted Graph Neural Networks
(GNNs), which process events as ``static" spatio-temporal graphs, which are
inherently "sparse". We take this trend one step further by introducing
Asynchronous, Event-based Graph Neural Networks (AEGNNs), a novel
event-processing paradigm that generalizes standard GNNs to process events as
``evolving" spatio-temporal graphs. AEGNNs follow efficient update rules that
restrict recomputation of network activations only to the nodes affected by
each new event, thereby significantly reducing both computation and latency for
event-by-event processing. AEGNNs are easily trained on synchronous inputs and
can be converted to efficient, "asynchronous" networks at test time. We
thoroughly validate our method on object classification and detection tasks,
where we show an up to a 11-fold reduction in computational complexity (FLOPs),
with similar or even better performance than state-of-the-art asynchronous
methods. This reduction in computation directly translates to an 8-fold
reduction in computational latency when compared to standard GNNs, which opens
the door to low-latency event-based processing.

本文提出采用异步事件驱动图神经网络 (AEGNN)，将事件处理范式从已知稀疏的静态图扩展到事件数据域中的一个演化的稀疏图，在物体分类和检测任务上，实现了计算复杂度提高 11 倍的同时，保持了最新的异步方法相同甚至更好的性能表现，将计算的降低转化为计算延迟降低了 8 倍，为低延时事件处理打开了新的机遇。