scholarly journals Content-Aware Attention Network for Action Recognition

2018 ◽  
pp. 109-120 ◽  
Author(s):  
Ziyi Liu ◽  
Le Wang ◽  
Nanning Zheng
Keyword(s):  
Action Recognition ◽  
Attention Network ◽  
Content Aware

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

Global and Local Knowledge-Aware Attention Network for Action Recognition

2021 ◽  
Vol 32 (1) ◽  
pp. 334-347 ◽  
Author(s):  
Zhenxing Zheng ◽  
Gaoyun An ◽  
Dapeng Wu ◽  
Qiuqi Ruan
Keyword(s):  
Action Recognition ◽  
Local Knowledge ◽  
Attention Network ◽  
Global And Local

Deep Attention Network for Egocentric Action Recognition

2019 ◽  
Vol 28 (8) ◽  
pp. 3703-3713 ◽  
Author(s):  
Minlong Lu ◽  
Ze-Nian Li ◽  
Yueming Wang ◽  
Gang Pan
Keyword(s):  
Action Recognition ◽  
Attention Network

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.

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
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