Recent　advances　in　event-based　research　prioritize　sparsity　and　temporal　precision.　Approaches　learning　sparse　point-based　representations　through　graph　CNNs　(GCN)　become　more　popular.　Yet,　these　graph　techniques　hold　lower　performance　than　their　frame-based　counterpart　due　to　two　issues:　(i)　Biased　graph　structures　that　don’t　properly　incorporate　varied　attributes　(such　as　semantics,　and　spatial　and　temporal　signals)　for　each　vertex,　resulting　in　inaccurate　graph　representations.　(ii)　A　shortage　of　robust　pretrained　models.　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.　©　2024,　Association　for　the　Advancement　of　Artificial　Intelligence　(www.aaai.org).　All　rights　reserved.