  Recent advances in event-based research prioritize sparsity and temporal
precision. Approaches using dense frame-based representations processed via
well-pretrained CNNs are being replaced by the use of sparse point-based
representations learned through graph CNNs (GCN). Yet, the efficacy of these
graph methods is far behind their frame-based counterparts with two
limitations. ($i$) Biased graph construction without carefully integrating
variant attributes ($i.e.$, semantics, spatial and temporal cues) for each
vertex, leading to imprecise graph representation. ($ii$) Deficient learning
because of the lack of well-pretrained models available. Here we solve the
first problem by proposing a new event-based GCN (EDGCN), with a dynamic
aggregation module to integrate all attributes of vertices adaptively. To
address the second problem, we introduce a novel learning framework called
cross-representation distillation (CRD), which leverages the dense
representation of events as a cross-representation auxiliary to provide
additional supervision and prior knowledge for the event graph. This
frame-to-graph distillation allows us to benefit from the large-scale priors
provided by CNNs while still retaining the advantages of graph-based models.
Extensive experiments show our model and learning framework are effective and
generalize well across multiple vision tasks.
