Bio-inspired neuromorphic cameras asynchronously record pixel brightness
changes and generate sparse event streams. They can capture dynamic scenes with
little motion blur and more details in extreme illumination conditions. Due to
the multidimensional address-event structure, most existing vision algorithms
cannot properly handle asynchronous event streams. While several event
representations and processing methods have been developed to address such an
issue, they are typically driven by a large number of events, leading to
substantial overheads in runtime and memory. In this paper, we propose a new
graph representation of the event data and couple it with a Graph Transformer
to perform accurate neuromorphic classification. Extensive experiments show
that our approach leads to better results and excels at the challenging
realistic situations where only a small number of events and limited
computational resources are available, paving the way for neuromorphic
applications embedded into mobile facilities.

提出了一种新的图表示方法将事件数据与图转换器相结合，用于准确的神经形态分类任务。通过广泛实验表明，该方法在仅有少量事件和有限计算资源的困难真实场景中表现出优异效果，为嵌入移动设备的神经形态应用开辟了道路。

基于图学习的神经形态成像和分类

Neuromorphic Imaging and Classification with Graph Learning

Event cameras are bio-inspired vision sensors that mimic retinas to
asynchronously report per-pixel intensity changes rather than outputting an
actual intensity image at regular intervals. This new paradigm of image sensor
offers significant potential advantages; namely, sparse and non-redundant data
representation. Unfortunately, however, most of the existing artificial neural
network architectures, such as a CNN, require dense synchronous input data, and
therefore, cannot make use of the sparseness of the data. We propose EventNet,
a neural network designed for real-time processing of asynchronous event
streams in a recursive and event-wise manner. EventNet models dependence of the
output on tens of thousands of causal events recursively using a novel temporal
coding scheme. As a result, at inference time, our network operates in an
event-wise manner that is realized with very few sum-of-the-product
operations---look-up table and temporal feature aggregation---which enables
processing of 1 mega or more events per second on standard CPU. In experiments
using real data, we demonstrated the real-time performance and robustness of
our framework.

本研究提出了 EventNet，一种基于新型视觉传感器的神经网络，旨在实时处理异步事件流，并采用了新的时间编码方案来建模输出对数万个因果事件的依赖关系，同时在标准 CPU 上每秒处理百万个事件，通过实验验证了该框架的实时性和稳健性。