Action Recognition with a Multi-View Temporal Attention Network

2021 ◽  
Author(s):  
Dengdi Sun ◽  
Zhixiang Su ◽  
Zhuanlian Ding ◽  
Bin Luo
Keyword(s):  
Action Recognition ◽  
Temporal Attention ◽  
Attention Network

Recurrent Spatial-Temporal Attention Network for Action Recognition in Videos

2018 ◽  
Vol 27 (3) ◽  
pp. 1347-1360 ◽  
Author(s):  
Wenbin Du ◽  
Yali Wang ◽  
Yu Qiao
Keyword(s):  
Action Recognition ◽  
Temporal Attention ◽  
Attention Network

Spatial-Temporal Attention Network with Multi-similarity Loss for Fine-Grained Skeleton-Based Action Recognition

2021 ◽  
pp. 620-631
Author(s):  
Xiang Li ◽  
Shenglan Liu ◽  
Yunheng Li ◽  
Hao Liu ◽  
Jinjing Zhao ◽  
...  
Keyword(s):  
Action Recognition ◽  
Temporal Attention ◽  
Attention Network ◽  
Fine Grained

scholarly journals Improved Action Recognition with Separable Spatio-Temporal Attention Using Alternative Skeletal and Video Pre-Processing

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1005
Author(s):  
Pau Climent-Pérez ◽  
Francisco Florez-Revuelta
Keyword(s):  
Activities Of Daily Living ◽  
Action Recognition ◽  
Assisted Living ◽  
Daily Living ◽  
State Of The Art ◽  
Temporal Attention ◽  
Attention Network ◽  
Full Activity ◽  
Potential Benefits ◽  
Spatio Temporal

The potential benefits of recognising activities of daily living from video for active and assisted living have yet to be fully untapped. These technologies can be used for behaviour understanding, and lifelogging for caregivers and end users alike. The recent publication of realistic datasets for this purpose, such as the Toyota Smarthomes dataset, calls for pushing forward the efforts to improve action recognition. Using the separable spatio-temporal attention network proposed in the literature, this paper introduces a view-invariant normalisation of skeletal pose data and full activity crops for RGB data, which improve the baseline results by 9.5% (on the cross-subject experiments), outperforming state-of-the-art techniques in this field when using the original unmodified skeletal data in dataset. Our code and data are available online.

scholarly journals R-STAN: Residual Spatial-Temporal Attention Network for Action Recognition

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 82246-82255 ◽  
Author(s):  
Quanle Liu ◽  
Xiangjiu Che ◽  
Mei Bie
Keyword(s):  
Action Recognition ◽  
Temporal Attention ◽  
Attention Network

Action recognition using geometric features and recurrent temporal attention network

2018 ◽  
Vol 26 (10) ◽  
pp. 2584-2591
Author(s):  
李庆辉 LI Qing-hui ◽  
李艾华 LI Ai-hua ◽  
郑 勇 ZHENG Yong ◽  
方 浩 FANG Hao
Keyword(s):  
Action Recognition ◽  
Geometric Features ◽  
Temporal Attention ◽  
Attention Network

Hierarchical Temporal Attention Network for Thyroid Nodule Recognition using Dynamic CEUS Imaging

2021 ◽  
pp. 1-1
Author(s):  
Peng Wan ◽  
Fang Chen ◽  
Chunrui Liu ◽  
Wentao Kong ◽  
Daoqiang Zhang
Keyword(s):  
Thyroid Nodule ◽  
Temporal Attention ◽  
Attention Network
Sign in / Sign up

Export Citation Format

Share Document