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- W3162714114 abstract "Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing convolutional operations to arbitrary non-regular domains. In particular, GCNs operating on spatial domains show superior performances compared to spectral ones, however their success is highly dependent on how the topology of input graphs is defined. In this paper, we introduce a novel framework for graph convolutional networks that learns the topological properties of graphs. The design principle of our method is based on the optimization of a constrained objective function which learns not only the usual convolutional parameters in GCNs but also a transformation basis that conveys the most relevant topological relationships in these graphs. Experiments conducted on the challenging task of skeleton-based action recognition shows the superiority of the proposed method compared to handcrafted graph design as well as the related work." @default.
- W3162714114 created "2021-05-24" @default.
- W3162714114 creator A5029896607 @default.
- W3162714114 date "2021-01-10" @default.
- W3162714114 modified "2023-10-14" @default.
- W3162714114 title "Learning Connectivity with Graph Convolutional Networks" @default.
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- W3162714114 doi "https://doi.org/10.1109/icpr48806.2021.9412009" @default.
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