Learning to share: The emergence of joint attention in human infancy.

2012 ◽  
pp. 173-210 ◽  
Author(s):  
Gedeon O. Deák ◽  
Jochen Triesch ◽  
Anna Krasno ◽  
Kaya de Barbaro ◽  
Marybel Robledo
Keyword(s):  
Joint Attention

Dolphin Social Cognition and Joint Attention: Our Current Understanding

2006 ◽  
Vol 32 (4) ◽  
pp. 443-460 ◽  
Author(s):  
Adam A. Pack ◽  
Louis M. Herman
Keyword(s):  
Social Cognition ◽  
Joint Attention ◽  
Current Understanding

scholarly journals Monocular Depth Estimation with Joint Attention Feature Distillation and Wavelet-Based Loss Function

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 54
Author(s):  
Peng Liu ◽  
Zonghua Zhang ◽  
Zhaozong Meng ◽  
Nan Gao
Keyword(s):  
Joint Attention ◽  
Loss Function ◽  
Depth Estimation ◽  
Depth Information ◽  
3D Vision ◽  
Network Training ◽  
Crucial Component ◽  
Benchmark Datasets ◽  
Ill Posed ◽  
Monocular Depth

Depth estimation is a crucial component in many 3D vision applications. Monocular depth estimation is gaining increasing interest due to flexible use and extremely low system requirements, but inherently ill-posed and ambiguous characteristics still cause unsatisfactory estimation results. This paper proposes a new deep convolutional neural network for monocular depth estimation. The network applies joint attention feature distillation and wavelet-based loss function to recover the depth information of a scene. Two improvements were achieved, compared with previous methods. First, we combined feature distillation and joint attention mechanisms to boost feature modulation discrimination. The network extracts hierarchical features using a progressive feature distillation and refinement strategy and aggregates features using a joint attention operation. Second, we adopted a wavelet-based loss function for network training, which improves loss function effectiveness by obtaining more structural details. The experimental results on challenging indoor and outdoor benchmark datasets verified the proposed method’s superiority compared with current state-of-the-art methods.

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