Multimodal Learning and Transfer Learning
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 April 2025 | Viewed by 2211
Special Issue Editors
Interests: zero-shot learning; domain adaptation; deep learning; machine learning; affective computing
Special Issues, Collections and Topics in MDPI journals
Interests: transfer learning; zero-shot learning
Special Issue Information
Dear Colleagues,
Multimodal learning and transfer learning, as popular research directions in the field of artificial intelligence, are leading the way in the development of artificial intelligence technology. Multimodal learning involves joint modeling and learning using data in different modalities (e.g., text, image, speech, etc.) to gain deeper understanding and reasoning capabilities. Transfer learning, on the other hand, explores how to transfer learned knowledge or experience to new tasks or domains to speed up the learning process and improve performance. This Special Issue brings together the latest research results and trends in multimodal learning and transfer learning and aims to provide a platform for academics and the industry to discuss these two fields in depth, to promote communication and cooperation in the fields of multimodal learning and transfer learning, and to facilitate the implementation of related technologies and innovations in practical applications.
We invite researchers from academia and industry to contribute their original research articles, reviews, and case studies. Topics of study can include, but are not limited to, the following:
- Multimodal learning models and algorithms;
- Cross-modal information fusion and representation learning;
- Theory and methods of migratory learning;
- Cross-domain transfer learning;
- Cross-language multimodal learning;
- Applications of multimodal learning and transfer learning (applied to natural language processing, image processing, audio processing and speech recognition, intelligent recommendation systems, etc.).
Dr. Yalan Ye
Dr. Jingjing Li
Dr. Shudong Huang
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
- multimodal learning
- transfer learning
- deep learning
- machine learning
- domain adaptation
- artificial intelligence
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