Probabilistic Models in Deep Learning and Computer Vision Tasks
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 30 June 2025 | Viewed by 1044
Special Issue Editors
Interests: machine learning; image restoration; computer vision
2. Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen 518060, China
Interests: anomaly detection; computer vision; machine learning
Special Issue Information
Dear Colleagues,
Deep learning has a wide range of applications in computer vision (such as image classification, object detection, semantic segmentation, medical image analysis, autonomous driving, etc.). By simulating the neural network structure of the human brain, it can automatically learn and extract the features of visual data, such as images and videos, and then perform complex visual tasks. Probabilistic models are an essential component of machine learning, which aims to learn patterns from data and make predictions on new, unseen data. To date, probabilistic models have been widely used in various applications like computer vision natural language processing tasks. Thus, developing probabilistic models can provide a powerful tool for machine learning tasks and help to solve complex problems.
Dr. Tao Dai
Dr. Jinbao Wang
Guest Editors
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Keywords
- deep learning
- computer vision
- optimization algorithm
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