Research on Machine Learning in Computer Vision
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 December 2024 | Viewed by 941
Special Issue Editors
Interests: computer science; feature extraction; deep learning; meta-learning; computer vision
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; human–computer interaction; human–machine cooperation; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue is dedicated to the exploration of the latest advancements in Machine Learning (ML) as they apply to computer vision. It is well known that the rapid progress and use of ML techniques have significantly enhanced the capabilities of computer vision systems, enabling them to interpret visual data with unprecedented effectiveness.
The aim of this Special Issue is to delve into and discuss how the most recent ML approaches, including but not limited to the field of deep learning, are being successfully applied to various computer vision tasks. These tasks include object detection, image retrieval, segmentation, recognition, and more.
We find particular interest in ML techniques such as meta-learning, reinforcement learning, and unsupervised and semi-supervised learning. We especially welcome contributions that address the challenges encountered in deploying these techniques, such as the demand for large datasets and high computational power, and that discuss and propose potential solutions, with a specific focus on one-shot or few-shot approaches. Moreover, contributions that highlight the impact of these advancements on various application domains, like healthcare, autonomous vehicles, and surveillance, are also welcomed.
Dr. Eleonora Iotti
Prof. Dr. João M. F. Rodrigues
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine learning
- computer vision
- one- and few-shot learning
- meta-learning
- reinforcement learning
- unsupervised and semi-supervised learning
- ML-based computer vision applications
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
This Special Issue is dedicated to the exploration of the latest advancements in Machine Learning (ML) as they apply to computer vision. It is well known that the rapid progress and use of ML techniques have significantly enhanced the capabilities of computer vision systems, enabling them to interpret visual data with unprecedented effectiveness.
The aim of this Special Issue is to delve into and discuss how the most recent ML approaches, including but not limited to the field of deep learning, are being successfully applied to various computer vision tasks. These tasks include object detection, image retrieval, segmentation, recognition, and more.
We find particular interest in ML techniques such as meta-learning, reinforcement learning, and unsupervised and semi-supervised learning. We especially welcome contributions that address the challenges encountered in deploying these techniques, such as the demand for large datasets and high computational power, and that discuss and propose potential solutions, with a specific focus on one-shot or few-shot approaches. Moreover, contributions that highlight the impact of these advancements on various application domains, like healthcare, autonomous vehicles, and surveillance, are also welcomed.
