Representing Videos as Discriminative Sub-graphs for Action Recognition

• URL: http://arxiv.org/abs/2201.04027v1
• Date: Tue, 11 Jan 2022 16:15:25 GMT
• Title: Representing Videos as Discriminative Sub-graphs for Action Recognition
• Authors: Dong Li and Zhaofan Qiu and Yingwei Pan and Ting Yao and Houqiang Li and Tao Mei
• Abstract summary: We introduce a new design of sub-graphs to represent and encode theriminative patterns of each action in the videos. We present MUlti-scale Sub-Earn Ling (MUSLE) framework that novelly builds space-time graphs and clusters into compact sub-graphs on each scale.
• Score: 165.54738402505194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 each scale with respect to the number of nodes. Technically, MUSLE produces 3D bounding boxes, i.e., tubelets, in each video clip, as graph nodes and takes dense connectivity as graph edges between tubelets. For each action category, we execute online clustering to decompose the graph into sub-graphs on each scale through learning Gaussian Mixture Layer and select the discriminative sub-graphs as action prototypes for recognition. Extensive experiments are conducted on both Something-Something V1 & V2 and Kinetics-400 datasets, and superior results are reported when comparing to state-of-the-art methods. More remarkably, our MUSLE achieves to-date the best reported accuracy of 65.0% on Something-Something V2 validation set.

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