scholarly journals An abbreviated implicit spatial context learning task that yields greater learning

2009 ◽  
Vol 41 (2) ◽  
pp. 391-395 ◽  
Author(s):  
Ilana J. Bennett ◽  
Kelly Anne Barnes ◽  
James H. Howard ◽  
Darlene V. Howard
Keyword(s):  
Learning Task ◽  
Spatial Context ◽  
Context Learning

Spatial context learning and switching strategy

2006 ◽  
Author(s):  
Nobutaka Endo ◽  
Walter R. Boot ◽  
Arthur F. Kramer ◽  
Alejandro Lleras ◽  
Takatsune Kumada
Keyword(s):  
Spatial Context ◽  
Switching Strategy ◽  
Context Learning

SCLNet: Spatial context learning network for congested crowd counting

2020 ◽  
Vol 404 ◽  
pp. 227-239 ◽  
Author(s):  
Shunzhou Wang ◽  
Yao Lu ◽  
Tianfei Zhou ◽  
Huijun Di ◽  
Lihua Lu ◽  
...  
Keyword(s):  
Spatial Context ◽  
Crowd Counting ◽  
Learning Network ◽  
Context Learning

1.198 The effect of Parkinson's disease on implicit spatial context learning

2007 ◽  
Vol 13 ◽  
pp. S59 ◽  
Author(s):  
M. van Asselen ◽  
C. Januário ◽  
A. Freire ◽  
R. André ◽  
I. Almeida ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Spatial Context ◽  
Context Learning

scholarly journals Spatial context learning in visual search and change detection

2005 ◽  
Vol 67 (7) ◽  
pp. 1128-1139 ◽  
Author(s):  
Yuhong Jiang ◽  
Joo-Hyun Song
Keyword(s):  
Visual Search ◽  
Change Detection ◽  
Spatial Context ◽  
Context Learning

scholarly journals Congested Crowd Counting via Adaptive Multi-Scale Context Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3777
Author(s):  
Yani Zhang ◽  
Huailin Zhao ◽  
Zuodong Duan ◽  
Liangjun Huang ◽  
Jiahao Deng ◽  
...  
Keyword(s):  
Density Estimation ◽  
State Of The Art ◽  
Spatial Context ◽  
Context Information ◽  
Crowd Counting ◽  
Multi Scale ◽  
Context Learning ◽  
Crowd Density Estimation ◽  
Crowd Density ◽  
Density Results

In this paper, we propose a novel congested crowd counting network for crowd density estimation, i.e., the Adaptive Multi-scale Context Aggregation Network (MSCANet). MSCANet efficiently leverages the spatial context information to accomplish crowd density estimation in a complicated crowd scene. To achieve this, a multi-scale context learning block, called the Multi-scale Context Aggregation module (MSCA), is proposed to first extract different scale information and then adaptively aggregate it to capture the full scale of the crowd. Employing multiple MSCAs in a cascaded manner, the MSCANet can deeply utilize the spatial context information and modulate preliminary features into more distinguishing and scale-sensitive features, which are finally applied to a 1 × 1 convolution operation to obtain the crowd density results. Extensive experiments on three challenging crowd counting benchmarks showed that our model yielded compelling performance against the other state-of-the-art methods. To thoroughly prove the generality of MSCANet, we extend our method to two relevant tasks: crowd localization and remote sensing object counting. The extension experiment results also confirmed the effectiveness of MSCANet.

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