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Temporal action proposal generation plays an important role in video actionunderstanding, which requires localizing high-quality action content precisely.However, generating temporal proposals with both precise boundaries andhigh-quality action content is extremely challenging. To address this issue, wepropose a novel Boundary Content Graph Neural Network (BC-GNN) to model theinsightful relations between the boundary and action content of temporalproposals by the graph neural networks. In BC-GNN, the boundaries and contentof temporal proposals are taken as the nodes and edges of the graph neuralnetwork, respectively, where they are spontaneously linked. Then a novel graphcomputation operation is proposed to update features of edges and nodes. Afterthat, one updated edge and two nodes it connects are used to predict boundaryprobabilities and content confidence score, which will be combined to generatea final high-quality proposal. Experiments are conducted on two mainstreamdatasets: ActivityNet-1.3 and THUMOS14. Without the bells and whistles, BC-GNNoutperforms previous state-of-the-art methods in both temporal action proposaland temporal action detection tasks.

Boundary Content Graph Neural Network for Temporal Action Proposal Generation