Multi-scale residual network model combined with Global Average Pooling for action recognition

2021 ◽  
Author(s):  
Jianjun Li ◽  
Yu Han ◽  
Ming Zhang ◽  
Gang Li ◽  
Baohua Zhang
Keyword(s):  
Action Recognition ◽  
Network Model ◽  
Residual Network ◽  
Global Average ◽  
Multi Scale

scholarly journals Action Recognition Based on CSI Signal Using Improved Deep Residual Network Model

2022 ◽  
Vol 130 (3) ◽  
pp. 1827-1851
Author(s):  
Jian Zhao ◽  
Shangwu Chong ◽  
Liang Huang ◽  
Xin Li ◽  
Chen He ◽  
...  
Keyword(s):  
Action Recognition ◽  
Network Model ◽  
Residual Network

scholarly journals Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network

2020 ◽  
Vol 10 (24) ◽  
pp. 9132
Author(s):  
Liguo Weng ◽  
Xiaodong Zhang ◽  
Junhao Qian ◽  
Min Xia ◽  
Yiqing Xu ◽  
...  
Keyword(s):  
Smart Grids ◽  
Recognition Rate ◽  
Low Frequency ◽  
Attention Mechanism ◽  
Learning Ability ◽  
Residual Network ◽  
Multi Scale ◽  
Energy Disaggregation ◽  
Benchmark Datasets ◽  
Load Disaggregation

Non-intrusive load disaggregation (NILD) is of great significance to the development of smart grids. Current energy disaggregation methods extract features from sequences, and this process easily leads to a loss of load features and difficulties in detecting, resulting in a low recognition rate of low-use electrical appliances. To solve this problem, a non-intrusive sequential energy disaggregation method based on a multi-scale attention residual network is proposed. Multi-scale convolutions are used to learn features, and the attention mechanism is used to enhance the learning ability of load features. The residual learning further improves the performance of the algorithm, avoids network degradation, and improves the precision of load decomposition. The experimental results on two benchmark datasets show that the proposed algorithm has more advantages than the existing algorithms in terms of load disaggregation accuracy and judgments of the on/off state, and the attention mechanism can further improve the disaggregation accuracy of low-frequency electrical appliances.

scholarly journals A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas

2018 ◽  
Vol 14 (10) ◽  
pp. e1006359 ◽  
Author(s):  
Maximilian Schmidt ◽  
Rembrandt Bakker ◽  
Kelly Shen ◽  
Gleb Bezgin ◽  
Markus Diesmann ◽  
...  
Keyword(s):  
Network Model ◽  
Resting State ◽  
Multi Scale ◽  
Cortical Areas ◽  
Scale Layer ◽  
Spiking Network ◽  
Visual Cortical
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