Multimodal Learning for Multimedia Content Analysis and Understanding
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".
Deadline for manuscript submissions: 10 March 2026 | Viewed by 36
Special Issue Editors
Interests: multimedia computing
Special Issue Information
Dear Colleagues,
With the explosive growth of multimedia data such as images, videos, audio, and text, the demand for effective multimedia content analysis and semantic understanding has become increasingly urgent. Traditional unimodal approaches often struggle to capture the rich and complementary information distributed across modalities. In order to solve this, researchers have increasingly turned to multimodal learning frameworks that facilitate joint representation and interaction among diverse data sources. This shift mainly relies on techniques like multimodal fusion, alignment, and semantic correlation modeling, which help better understand multimodal data and improve performance in tasks such as multimodal fusion, multimodal retrieval, caption generation, and visual–language inference. However, challenges such as modality imbalance, multimodal misalignment, and domain adaptability remain open problems. In light of these challenges, this Special Issue aims to showcase recent progress and emerging trends in multimedia content analysis and understanding, with a particular emphasis on robust, scalable, and generalizable multimodal solutions. We invite contributions that not only propose novel models and algorithms but also address practical deployment issues, offering insights into how multimodal systems can be applied effectively in real-world scenarios.
We look forward to receiving your contributions.
Dr. Donglin Zhang
Dr. Zhen Liu
Guest Editors
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Keywords
- multimodal fusion
- vision–language alignment
- modality imbalance
- domain adaptation
- representation learning
- multimedia retrieval
- multimedia intelligence
- multimedia-related applications
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