scholarly journals Detail texture detection based on Yolov4‐tiny combined with attention mechanism and bicubic interpolation

2021 ◽  
Author(s):  
Tian Hui ◽  
YueLei Xu ◽  
Rasol Jarhinbek
Keyword(s):  
Attention Mechanism ◽  
Texture Detection ◽  
Bicubic Interpolation

An Attention Mechanism Extension of Automatic HTML Generation from Web Page Design Images

2020 ◽  
Vol 140 (12) ◽  
pp. 1393-1401
Author(s):  
Hiroki Chinen ◽  
Hidehiro Ohki ◽  
Keiji Gyohten ◽  
Toshiya Takami
Keyword(s):  
Attention Mechanism ◽  
Web Page ◽  
Web Page Design ◽  
Page Design

Online Speaker Adaptation for LVCSR Based on Attention Mechanism

2018 ◽  
Author(s):  
Jia Pan ◽  
Diyuan Liu ◽  
Genshun Wan ◽  
Jun Du ◽  
Qingfeng Liu ◽  
...  
Keyword(s):  
Attention Mechanism

scholarly journals Dam Deformation Interpretation and Prediction Based on a Long Short-Term Memory Model Coupled with an Attention Mechanism

2021 ◽  
Vol 11 (14) ◽  
pp. 6625
Author(s):  
Yan Su ◽  
Kailiang Weng ◽  
Chuan Lin ◽  
Zeqin Chen
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Impact Factors ◽  
Nonlinear Prediction ◽  
Time Dimension ◽  
Short Term ◽  
Deformation Prediction ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Dam Deformation

An accurate dam deformation prediction model is vital to a dam safety monitoring system, as it helps assess and manage dam risks. Most traditional dam deformation prediction algorithms ignore the interpretation and evaluation of variables and lack qualitative measures. This paper proposes a data processing framework that uses a long short-term memory (LSTM) model coupled with an attention mechanism to predict the deformation response of a dam structure. First, the random forest (RF) model is introduced to assess the relative importance of impact factors and screen input variables. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) method is used to identify and filter the equipment based abnormal values to reduce the random error in the measurements. Finally, the coupled model is used to focus on important factors in the time dimension in order to obtain more accurate nonlinear prediction results. The results of the case study show that, of all tested methods, the proposed coupled method performed best. In addition, it was found that temperature and water level both have significant impacts on dam deformation and can serve as reliable metrics for dam management.

A Review on the Attention Mechanism of Deep Learning

2021 ◽  
Author(s):  
Zhaoyang Niu ◽  
Guoqiang Zhong ◽  
Hui Yu
Keyword(s):  
Deep Learning ◽  
Attention Mechanism

Infrared thermal imaging denoising method based on second-order channel attention mechanism

2021 ◽  
pp. 103789
Author(s):  
Zhuo Li ◽  
Shaojuan Luo ◽  
Meiyun Chen ◽  
Heng Wu ◽  
Tao Wang ◽  
...  
Keyword(s):  
Thermal Imaging ◽  
Second Order ◽  
Attention Mechanism ◽  
Denoising Method ◽  
Infrared Thermal Imaging

scholarly journals Attention Mechanism based Real Time Gaze Tracking in Natural Scenes with Residual Blocks

2021 ◽  
pp. 1-1
Author(s):  
Lihong Dai ◽  
Jinguo Liu ◽  
Zhaojie Ju ◽  
Yang Gao
Keyword(s):  
Real Time ◽  
Attention Mechanism ◽  
Natural Scenes ◽  
Gaze Tracking
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