3D Attention U-Net with Pretraining: A Solution to CADA-Aneurysm Segmentation Challenge

2021 ◽  
pp. 58-67
Author(s):  
Ziyu Su ◽  
Yizhuan Jia ◽  
Weibin Liao ◽  
Yi Lv ◽  
Jiaqi Dou ◽  
...  
Keyword(s):  
3D Attention

Measurement on Reaction Time of Visual Attention in Depth during Driving

2013 ◽  
Vol 319 ◽  
pp. 343-347
Author(s):  
Ru Ting Xia ◽  
Xiao Yan Zhou
Keyword(s):  
Reaction Time ◽  
Visual Attention ◽  
Measurement System ◽  
Low Vision ◽  
Three Dimensional ◽  
Depth Information ◽  
Attention Shifting ◽  
3D Space ◽  
Traffic Environment ◽  
3D Attention

This research aimed to reveal characteristics of visual attention of low-vision drivers. Near and far stimuli were used by means of a three-dimensional (3D) attention measurement system that simulated traffic environment. We measured the reaction time of subjects while attention shifted in three kinds of imitational peripheral environment illuminance (daylight, twilight and dawn conditions). Subjects were required to judge whether the target presented nearer than fixation point or further than it. The results showed that the peripheral environment illuminance had evident influence on the reaction time of drivers, the reaction time was slow in dawn and twilight conditions than in daylight condition, distribution of attention had the advantage in nearer space than farther space, that is, and the shifts of attention in 3D space had an anisotropy characteristic in depth. The results suggested that (1) visual attention might be operated with both precueing paradigm and stimulus controls included the depth information, (2) an anisotropy characteristic of attention shifting depend on the attention moved distance, and it showed remarkably in dawn condition than in daylight and twilight conditions.

Detect and Identify Aneurysms Based on Adjusted 3D Attention UNet

2021 ◽  
pp. 39-48
Author(s):  
Yizhuan Jia ◽  
Weibin Liao ◽  
Yi Lv ◽  
Ziyu Su ◽  
Jiaqi Dou ◽  
...  
Keyword(s):  
3D Attention

scholarly journals Super-Resolution for Hyperspectral Remote Sensing Images Based on the 3D Attention-SRGAN Network

2020 ◽  
Vol 12 (7) ◽  
pp. 1204
Author(s):  
Xinyu Dou ◽  
Chenyu Li ◽  
Qian Shi ◽  
Mengxi Liu
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Super Resolution ◽  
Hyperspectral Remote Sensing ◽  
Spectral Information ◽  
Remote Sensing Images ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Hyperspectral Remote Sensing Images ◽  
3D Attention

Hyperspectral remote sensing images (HSIs) have a higher spectral resolution compared to multispectral remote sensing images, providing the possibility for more reasonable and effective analysis and processing of spectral data. However, rich spectral information usually comes at the expense of low spatial resolution owing to the physical limitations of sensors, which brings difficulties for identifying and analyzing targets in HSIs. In the super-resolution (SR) field, many methods have been focusing on the restoration of the spatial information while ignoring the spectral aspect. To better restore the spectral information in the HSI SR field, a novel super-resolution (SR) method was proposed in this study. Firstly, we innovatively used three-dimensional (3D) convolution based on SRGAN (Super-Resolution Generative Adversarial Network) structure to not only exploit the spatial features but also preserve spectral properties in the process of SR. Moreover, we used the attention mechanism to deal with the multiply features from the 3D convolution layers, and we enhanced the output of our model by improving the content of the generator’s loss function. The experimental results indicate that the 3DASRGAN (3D Attention-based Super-Resolution Generative Adversarial Network) is both visually quantitatively better than the comparison methods, which proves that the 3DASRGAN model can reconstruct high-resolution HSIs with high efficiency.

3D attention-driven depth acquisition for object identification

2016 ◽  
Vol 35 (6) ◽  
pp. 1-14 ◽  
Author(s):  
Kai Xu ◽  
Yifei Shi ◽  
Lintao Zheng ◽  
Junyu Zhang ◽  
Min Liu ◽  
...  
Keyword(s):  
Object Identification ◽  
3D Attention

3D attention

2013 ◽  
Author(s):  
Lucas Paletta ◽  
Katrin Santner ◽  
Gerald Fritz ◽  
Heinz Mayer ◽  
Johann Schrammel
Keyword(s):  
3D Attention

Fine-Grained 3D-Attention Prototypes for Few-Shot Learning

2020 ◽  
Vol 32 (9) ◽  
pp. 1664-1684
Author(s):  
Xin Hu ◽  
Jun Liu ◽  
Jie Ma ◽  
Yudai Pan ◽  
Lingling Zhang
Keyword(s):  
Image Reconstruction ◽  
Feature Learning ◽  
Local Features ◽  
Image Features ◽  
Superior Performance ◽  
Fine Grained ◽  
Learning Module ◽  
Class Distribution ◽  
Label Distribution ◽  
3D Attention

In the real world, a limited number of labeled finely grained images per class can hardly represent the class distribution effectively. Due to the more subtle visual differences in fine-grained images than simple images with obvious objects, that is, there exist smaller interclass and larger intraclass variations. To solve these issues, we propose an end-to-end attention-based model for fine-grained few-shot image classification (AFG) with the recent episode training strategy. It is composed mainly of a feature learning module, an image reconstruction module, and a label distribution module. The feature learning module mainly devises a 3D-Attention mechanism, which considers both the spatial positions and different channel attentions of the image features, in order to learn more discriminative local features to better represent the class distribution. The image reconstruction module calculates the mappings between local features and the original images. It is constrained by a designed loss function as auxiliary supervised information, so that the learning of each local feature does not need extra annotations. The label distribution module is used to predict the label distribution of a given unlabeled sample, and we use the local features to represent the image features for classification. By conducting comprehensive experiments on Mini-ImageNet and three fine-grained data sets, we demonstrate that the proposed model achieves superior performance over the competitors.

scholarly journals 3D Attention M-net for Short-axis Left Ventricular Myocardium Segmentation in Mice MR cardiac Images

2021 ◽  
Author(s):  
Luojie Huang ◽  
Andrew Jin ◽  
Jinchi Wei ◽  
Dnyanesh Tipre ◽  
Chin-Fu Liu ◽  
...  
Keyword(s):  
Ventricular Myocardium ◽  
Left Ventricular ◽  
Left Ventricular Myocardium ◽  
Short Axis ◽  
Cardiac Images ◽  
3D Attention
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