Boundary graph convolutional network for temporal action detection

作者：

Highlights：

• GCN based on boundary generation for densely produce the action proposals

• Efficient and novel BGCN model has a great capability to learn the proposal features.

• Has a lower model size for temporal action proposals generation

• Has fast inference time for temporal action proposals generation

摘要

•GCN based on boundary generation for densely produce the action proposals•Efficient and novel BGCN model has a great capability to learn the proposal features.•Has a lower model size for temporal action proposals generation•Has fast inference time for temporal action proposals generation

论文关键词：Temporal action detection,Graph convolutional network,Temproal action proposals,Video features

论文评审过程：Received 11 September 2020, Revised 12 January 2021, Accepted 16 February 2021, Available online 20 February 2021, Version of Record 3 March 2021.

论文官网地址：https://doi.org/10.1016/j.imavis.2021.104144