  Event cameras attract researchers' attention due to their low power
consumption, high dynamic range, and extremely high temporal resolution.
Learning models on event-based object classification have recently achieved
massive success by accumulating sparse events into dense frames to apply
traditional 2D learning methods. Yet, these approaches necessitate heavy-weight
models and are with high computational complexity due to the redundant
information introduced by the sparse-to-dense conversion, limiting the
potential of event cameras on real-life applications. This study aims to
address the core problem of balancing accuracy and model complexity for
event-based classification models. To this end, we introduce a novel graph
representation for event data to exploit their sparsity better and customize a
lightweight voxel graph convolutional neural network (\textit{EV-VGCNN}) for
event-based classification. Specifically, (1) using voxel-wise vertices rather
than previous point-wise inputs to explicitly exploit regional 2D semantics of
event streams while keeping the sparsity;(2) proposing a multi-scale feature
relational layer (\textit{MFRL}) to extract spatial and motion cues from each
vertex discriminatively concerning its distances to neighbors. Comprehensive
experiments show that our model can advance state-of-the-art classification
accuracy with extremely low model complexity (merely 0.84M parameters).
