Multi-Modal Learning for Multimedia Data Analysis and Applications
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 5319
Special Issue Editors
Interests: machine learning; pattern recognition; multi-modal learning
Special Issue Information
Dear Colleagues,
The advance of data collection, transmission and storage has generated the explosive growth of multi-source, multi-view, or multi-modal multimedia data, such as texts, images, audios and videos. For example, magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET) have been explored for disease diagnosis in clinical practice. Face image, fingerprint, palm print, and iris have been used for face recognition. Object tracking exploits RGB images, pseudo depth images and thermal infrared images to improve reliability and accuracy of autonomous driving systems. Various multi-modal learning methods have been proposed, including graph-based, kernel-based, subspace learning-based, manifold learning-based, ensemble learning-based methods, as well as their deep extensions.
Despite having many benefits, there still exists several open and unexplored challenges. For example, most current multi-modal learning methods may suffer from quadratic or even cubic complexity when encountering large-scale datasets. This may restrict their practical applicability. In disease diagnosis, patients may not do all medical tests, leading to the incomplete multi-modal learning problem. Deep multi-modal learning has achieved promising performance, yet lack of good interpretability. These are because it is always challenging to fuse multi-modal multimedia data across different modalities for subsequent applications such as recommendation, emotion recognition, matching, classification.
This special issue seeks the latest advances towards novel theory, architecture and algorithm design in multi-modal data analysis for pattern recognition, computer vision, and their novel applications such as multi-modal outlier detection, multi- modal object tracking, multi-model medical analysis. We hope these advances can improve the accuracy, robustness, and efficiency of multi-modal data analysis. Therefore, papers of the above topics as well as on other related topics are welcome. The following lists contain topics of interest (but not limited to):
- Novel multi-modal learning for multimedia applications such as multi-modal outlier detection, multi-modal object tracking, multi-model medical analysis
- Novel multi-modal learning theories
- Optimization for multi-modal multimedia data analysis techniques
- Fast solvers for large-scale multi-modal data
- Incomplete multi-modal learning methods
- Unsupervised/semi-supervised multi-modal learning methods
- Deep multi-modal learning methods
- Incorporating new mathematical techniques in multi-modal data analysis
Dr. Yongyong Chen
Dr. Yongqiang Tang
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. Electronics 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
- unsupervised/semi-supervised multi-modal learning
- deep learning
- multimedia data analysis
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.