Representing Videos as Discriminative Sub-graphs for Action Recognition. (arXiv:2201.04027v1 [cs.CV])

Human actions are typically of combinatorial structures or patterns, i.e.,
subjects, objects, plus spatio-temporal interactions in between. Discovering
such structures is therefore a rewarding way to reason about the dynamics of
interactions and recognize the actions. In this paper, we introduce a new
design of sub-graphs to represent and encode the discriminative patterns of
each action in the videos. Specifically, we present MUlti-scale Sub-graph
LEarning (MUSLE) framework that novelly builds space-time graphs and clusters
the graphs into compact sub-graphs on …

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