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• W2894982307 endingPage "689" @default.
• W2894982307 startingPage "673" @default.
• W2894982307 abstract "We propose Neural Graph Matching (NGM) Networks, a novel framework that can learn to recognize a previous unseen 3D action class with only a few examples. We achieve this by leveraging the inherent structure of 3D data through a graphical representation. This allows us to modularize our model and lead to strong data-efficiency in few-shot learning. More specifically, NGM Networks jointly learn a graph generator and a graph matching metric function in an end-to-end fashion to directly optimize the few-shot learning objective. We evaluate NGM on two 3D action recognition datasets, CAD-120 and PiGraphs, and show that learning to generate and match graphs both lead to significant improvement of few-shot 3D action recognition over the holistic baselines." @default.
• W2894982307 created "2018-10-12" @default.
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• W2894982307 date "2018-01-01" @default.
• W2894982307 modified "2023-10-17" @default.
• W2894982307 title "Neural Graph Matching Networks for Fewshot 3D Action Recognition" @default.
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• W2894982307 doi "https://doi.org/10.1007/978-3-030-01246-5_40" @default.
• W2894982307 hasPublicationYear "2018" @default.
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