.png){kind=icon}
返回
Nonlinear gated channels networks for action recognition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2019.12.077.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Video-based convolutional neural networks (CNNs) involve a large amount of training parameters, leading to the enormous computational complexity, which thereby delays the network convergence. Therefore, training successful CNN for action recognition rapidly is non-trivial. In this paper, a novel encoding called nonlinear gated channels unit (NGCU) is proposed to facilitate network training by encoding the global channel-level relationship. Based on this, nonlinear gated channels network (NGCN) is constructed for the end-to-end encoding, and the corresponding convergence performance is evaluated on the standard benchmarks UCF101 and HMDB51. Experimental results demonstrate that the proposed method is conducive to the convergence process of CNN based action recognition models. (c) 2019 Published by Elsevier B.V.
Keyword:
Convolutional neural network
Action recognition
Network training
Network convergence
Long range temporal property
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
.png){kind=icon}
查看更多内容