A Dynamic Graph CNN with Cross-Representation Distillation for Event-Based Recognition

• URL: http://arxiv.org/abs/2302.04177v2
• Date: Sun, 16 Apr 2023 08:14:29 GMT
• Title: A Dynamic Graph CNN with Cross-Representation Distillation for Event-Based Recognition
• Authors: Yongjian Deng, Hao Chen, Bochen Xie, Hai Liu, Youfu Li
• Abstract summary: We present a new event-based graph learning framework called graph cross-representation distillation (CRD) CRD provides additional supervision and prior knowledge for the event graph. Our model and learning framework are effective and generalize well across multiple vision tasks.
• Score: 21.225945234873745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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