Low-Quality Multimodal Data Fusion: Methodologies and Applications

Dear Colleagues,

In the big data era, a wide array of multi-source and heterogeneous data have been generated in multiple modalities. Generally speaking, different modalities represent data samples from different perspectives which usually provide complementary information to each other. By exploiting the complementary characteristics, this approach can provide a more comprehensive representation of data samples and uncover the vast value hidden within large datasets, thereby sypporting humans in various domains, such as intelligent decision-making and predictive services. Data fusion is a powerful tool for multimodal data analysis and mining; however, in many real-world big data analytic applications, the low-quality characteristics of multimodal data, such as inaccuracy, incompleteness, and unbalance, pose great challenges to the design of data fusion methods. Thus, integrating knowledge across modalities and unlocking the value hidden in low-quality data remains a significant challenge. To address it, this Special Issue seeks high-quality papers from academics and industry-related researchers of multimodal data fusion, machine learning, and artificial intelligence. We invite submissions that present the latest advances in methods and applications aimed at enabling effective fusion of low-quality multimodal data.

Proposed submissions should be original, unpublished, and novel for in-depth research. Topics of interest for This Special issue include, but are not limited to, the following: 

• Low-quality multimodal data fusion and analysis;
• Data availability theory for multimodal data;
• Multimodal feature learning;
• Incomplete multimodal data fusion;
• Few-shot cross-modal retrieval;
• Domain adaption and transfer learning;
• Meta learning for low-quality multimodal data;
• Deep neural networks for low-quality multimodal data;
• Graph theory for low-quality multimodal data;
• Knowledge discovery for low-quality multimodal data;
• AI methods for multimodal data;
• Industrial applications based on low-quality multimodal data fusion;
• Medical applications based on low-quality multimodal data fusion;
• Other advanced applications based on low-quality multimodal data fusion.

Dr. Liang Zhao
Prof. Dr. Qingchen Zhang
Dr. Boxiang Dong
Guest Editors

Manuscript Submission Information

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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. Mathematics 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 2600 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.

• low-quality multimodal data fusion and analysis
• data availability theory for multimodal data
• multimodal feature learning
• incomplete multimodal data fusion
• few-shot cross-modal retrieval
• domain adaption and transfer learning
• meta learning for low-quality multimodal data
• deep neural networks for low-quality multimodal data
• graph theory for low-quality multimodal data
• knowledge discovery for low-quality multimodal data
• AI methods for multimodal data
• industrial applications based on low-quality multimodal data fusion
• medical applications based on low-quality multimodal data fusion
• other advanced applications based on low-quality multimodal data fusion