GRNet: An Efficient Group Ranking Network for Face Attribute Recognition

2019 ◽  
Author(s):  
Dingchang Hu ◽  
Pingyu Wang ◽  
Yiwei Liu ◽  
Fei Su ◽  
Zhicheng Zhao
Keyword(s):  
Group Ranking ◽  
Attribute Recognition

Correlation Graph Convolutional Network for Pedestrian Attribute Recognition

2020 ◽  
pp. 1-1
Author(s):  
Haonan Fan ◽  
Hai-Miao Hu ◽  
Shuailing Liu ◽  
Weiqing Lu ◽  
Shiliang Pu
Keyword(s):  
Convolutional Network ◽  
Correlation Graph ◽  
Attribute Recognition

Development of an Evaluation Model of Freeway Traffic Safety Facilities System

2013 ◽  
Vol 734-737 ◽  
pp. 1578-1581
Author(s):  
Yan Yong Guo ◽  
Yao Wu ◽  
Liang Song ◽  
Hui Duan
Keyword(s):  
Traffic Safety ◽  
Evaluation System ◽  
Evaluation Model ◽  
Variation Coefficient ◽  
Freeway Traffic ◽  
Evaluation Result ◽  
Single Index ◽  
Recognition Theory ◽  
Attribute Measure ◽  
Attribute Recognition

This study developed an evaluation model of freeway traffic safety facilities system. Firstly, an evaluation system of freeway traffic safety facility was proposed. Secondly, an evaluation model was proposed based on attribute recognition theory. And the evaluation result was identified according to the attribute measure value of single index and the comprehensive attribute measure value of multiple indexes as well as the confidence criterion. Thirdly, the weight of each indicator was decided by variation coefficient. Finally, A case of TAI-GAN freeway (K1+242~K3+259 segment) was conducted to verify the feasibility and effectiveness of the model.

Attribute Recognition Model Based on Entropy Weight and Its Application to Evaluation of Groundwater Quality

2010 ◽  
Vol 29-32 ◽  
pp. 2698-2702
Author(s):  
Xian Qi Zhang ◽  
Wen Hong Feng ◽  
Nan Nan Li
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Information Entropy ◽  
Simple Calculation ◽  
Weight Coefficient ◽  
Entropy Theory ◽  
Entropy Weight ◽  
Recognition Model ◽  
Model Based ◽  
Attribute Recognition

It is necessary to take into account synthetically attribute of every index because of independence and incompatibility resulted from single index evaluating outcomes. Through the information entropy theory and attribute recognition model being combined together, attribute recognition model based on entropy weight is constructed and applied to evaluating groundwater quality by a new method, weight coefficient by the law of entropy value is exercised so that it is more objective. The outcome from concrete application indicates that it is suitable to evaluate water quality with reasonable conclusion and simple calculation.

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