Advances in Multimodal AI: Challenges and Opportunities
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 July 2026 | Viewed by 257
Special Issue Editors
Interests: graph neural networks; multimodal AI; salient object detection; semantic segmentation; deep learning; computer vision
Special Issue Information
Dear Colleagues,
We invite researchers, practitioners, and industry experts to submit their original research and innovative solutions to this Special Issue, titled “Advances in Multimodal AI: Challenges and Opportunities”. This issue aims to showcase cutting-edge developments in the rapidly evolving field of multimodal artificial intelligence, with a focus on novel theories, models, algorithms, systems, and applications that integrate multiple data modalities.
As multimodal AI continues to gain prominence across natural language processing, computer vision, audio understanding, robotics, and embodied intelligence, new opportunities and challenges have emerged. These include designing unified model architectures, improving cross-modal alignment, enhancing model interpretability, ensuring robustness in real-world deployment, and establishing comprehensive evaluation benchmarks. This Special Issue seeks to explore the latest advancements, fundamental challenges, and future research directions in multimodal learning and multimodal large models.
Potential topics include, but are not limited to, the following areas:
- Multimodal representation learning and fusion;
- Cross-modal alignment, grounding, and semantic consistency;
- Multimodal large language models (MLLMs) and foundation models;
- Vision–language understanding and generation;
- Audio–visual learning, speech–vision integration, and cross-modal retrieval;
- Multimodal reasoning, instruction following, and agent-based interactions;
- Efficient training, optimization, and deployment of multimodal systems;
- Safety, robustness, and bias mitigation in multimodal AI;
- Evaluation metrics, benchmarks, and emergent capabilities;
- Human–AI collaboration, interactive multimodal interfaces, and XR applications;
- Applications of multimodal AI in healthcare, autonomous systems, industrial inspection, education, and other domains.
This Special Issue welcomes theoretical contributions, methodological innovations, system-level implementations, and real-world case studies that push the boundaries of multimodal AI. We look forward to hearing from you.
Dr. Gaowei Zhang
Dr. Wei Wang
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 250 words) can be sent to the Editorial Office for assessment.
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
- cross-modal representation learning
- cross-modal alignment
- real-world multimodal applications
- multimodal reasoning
- multimodal generation
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