Deep Learning in Computer Vision: Methods, Interpretation, Causation, and Fairness

2019 ◽  
pp. 73-100 ◽  
Author(s):  
Nikhil Malik ◽  
Param Vir Singh
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Learning In Computer Vision

The application of deep learning in computer vision

2017 ◽  
Author(s):  
Qing Wu ◽  
Yungang Liu ◽  
Qiang Li ◽  
Shaoli Jin ◽  
Fengzhong Li
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Learning In Computer Vision

Deep Learning in Computer Vision Through Mobile Edge Computing for IoT

2021 ◽  
pp. 443-471
Author(s):  
Abu Sufian ◽  
Ekram Alam ◽  
Anirudha Ghosh ◽  
Farhana Sultana ◽  
Debashis De ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Learning In Computer Vision

scholarly journals CVFlow: Open Workflow for Computer Vision

2019 ◽  
Author(s):  
Xiaozhe Yao
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Image Annotation ◽  
Learning System ◽  
Commercial Products ◽  
Learning In Computer Vision ◽  
Instance Segmentation ◽  
Image Caption

In recent years, the multimedia community has witnessed an emerging usage of deep learning in computer vision, such as image caption\cite{Vinyals_2015_CVPR}, object detection, instance segmentation and etc, and many companies are adopting deep learning into their commercial products. Developing and Deploying a fast and reliable deep learning system is complex and involves labour-intensive works, like image annotation\cite{Ratner_2017}, data and models versioning and etc. To facilitate such a process, we proposed CVFlow, which is a suite of versatile computer vision libraries and toolkits for handling computer vision tasks, including intelligent annotation toolkit, deep learning package manager, models sharing platform and computer vision serving program.

Deep Learning in Computer Vision

2020 ◽  
Author(s):  
Mahmoud Hassaballah ◽  
Ali Ismail Awad
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Learning In Computer Vision

scholarly journals Synthetic datasets for Deep Learning in computer-vision assisted tasks in manufacturing

Procedia CIRP ◽  
2021 ◽  
Vol 103 ◽  
pp. 237-242
Author(s):  
Christos Manettas ◽  
Nikolaos Nikolakis ◽  
Kosmas Alexopoulos
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Synthetic Datasets ◽  
Learning In Computer Vision

scholarly journals A novel multi-stream method for violent interaction detection using deep learning

2020 ◽  
Vol 53 (5-6) ◽  
pp. 796-806
Author(s):  
Hongchang Li ◽  
Jing Wang ◽  
Jianjun Han ◽  
Jinmin Zhang ◽  
Yushan Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Optical Flow ◽  
High Probability ◽  
Recognition Performance ◽  
Surveillance Video ◽  
Interaction Detection ◽  
Temporal Features ◽  
Violent Action ◽  
Learning In Computer Vision

Violent interaction detection is a hot topic in computer vision. However, the recent research works on violent interaction detection mainly focus on the traditional hand-craft features, and does not make full use of the research results of deep learning in computer vision. In this paper, we propose a new robust violent interaction detection framework based on multi-stream deep learning in surveillance scene. The proposed approach enhances the recognition performance of violent action in video by fusing three different streams: attention-based spatial RGB stream, temporal stream, and local spatial stream. The attention-based spatial RGB stream learns the spatial attention regions of persons that have high probability to be action region through soft-attention mechanism. The temporal stream employs optical flow as input to extract temporal features. The local spatial stream learns spatial local features using block images as input. Experimental results demonstrate the effectiveness and reliability of the proposed method on three violent interactive datasets: hockey fights, movies, violent interaction. We also verify the proposed method on our own elevator surveillance video dataset and the performance of the proposed method is satisfied.

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