Towards Label-free Learning for Industrial Vision Applications
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 522
Special Issue Editors
Interests: computer science; computer vision; deep learning; machine learning
Interests: machine learning; computer vision; bayesian nonparametrics; kernel method
Interests: computer vision; machine learning; pattern recognition; data mining; bioinformatics; medicine
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent trends in machine learning and computer vision technology have effectively boosted the application of industrial visual inspection. However, most of the available systems work in a human-in-the-loop manner and still require hand-crafted labels for training supervised AI models. In order to take a step forwards, there is a need to reduce manual intervention and automatic incremental iterative AI models for a complex production system.
We are pleased to invite you to publish theoretical advancements about label-free machine learning manners on a variety of topics, including unsupervised learning, self-supervised learning, semi-supervised learning, weakly supervised learning, few-shot learning, incremental learning, and transfer learning. Furthermore, the latest strides in computer vision have the potential to be applied in a variety of domains. We also invite you to publish novel practical developments, especially on anomaly detection, intrusion detection, hand pose estimation, pose estimation, video surveillance monitoring, medical imaging diagnostics, industrial manufacturing applications, and software tools for label-free machine learning.
This Special Issue aims to promote high-quality and original research papers on the above-described areas. Topics of interest include, without being limited to:
- Label-free machine learning manners without human-in-the-loop.
- Software tools on label-free learning for industrial vision application.
- Unsupervised, semi-supervised, self-supervised, and weakly supervised learning methods.
- Transfer, low-shot, few-shot, incremental, continual, and long-tailed learning methods.
- Two/three-dimensional anomaly detection; video intrusion detection applications.
- Video surveillance monitoring; human pose estimation applications.
- Ultrasound/X-ray/MRI imaging-assisted medical or industrial diagnostics.
- Production system automation and incremental iterative learning.
Dr. Jiawei Li
Dr. Naiqi Li
Dr. Ognjen Arandjelović
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- deep learning
- computer vision
- unsupervised learning
- self-supervised learning
- industrial anomaly detection
- video signal processing
- medical imaging diagnostics
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