Guest Editors
Prof. Yong Zhang
Email: zhangyong2010@bjut.edu.cn
Affiliation: School of Information Science and Technology, Beijing University of Technology, Beijing, China
Homepage:
Research Interests: Intelligent Transportation Systems, Big Data Analysis,
Visualization, Computer Graphics
Prof. Yanming Shen
Email: shen@dlut.edu.cn
Affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China
Homepage:
Research Interests: Graph Machine Learning, Knowledge Graph, Big data
Assist. Prof. Ruihai Dong
Email: ruihai.dong@ucd.ie
Affiliation: School of Computer Science, University College Dublin, Dublin, D04 V1W8, Ireland
Homepage:
Research Interests: Recommender Systems, Data Analytics, Deep Learning and Machine Learning
Dr. Ye Yuan
Email: yey@ieee.org
Affiliation: The College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
Homepage:
Research Interests: Deep learning, Aviation network optimization, Intelligent transportation system
Summary
Large language models (LLMs) have attracted widespread attention in the field of natural language processing and have demonstrated promising applications in various domains such as transportation, meteorology, agriculture, and finance. However, LLMs themselves present numerous challenges, such as the generation of false information and poor model interpretability. On the other hand, the complex relationships and complementary nature of cross-modal data provide a new research entry point for generative AI applications. Domain-based intelligent systems are no longer limited to processing single data sets; they are now capable of integrating and processing text, images, audio, video, and sensor data. The research and application of cross-modal models are gaining increasing attention in academia and industry.
In-depth research on LLMs, multimodal large models, and related applications is crucial for promoting cross-domain knowledge integration and innovation, improving intelligence, optimizing decision-making and business processes, and accelerating the intelligent upgrade of related industries. The innovative application of multimodal large models in related fields holds broad prospects and challenges. With the continuous advancement of technology, we hope to see more research in the future to further advance this field.
Therefore, this special issue focuses on the research progress of large models and their field applications. The following subtopics are the particular interests of this special issue, including but not limited to:
· Foundation Models and Pre-training Strategies
· Advanced Fine-tuning and Adaptation Methods for Large Models
· Evaluation, Interpretability and Trustworthiness of Large Models
· Multimodal Knowledge Graphs and Model Augmentation
· Domain-specific Applications of Large Models across Industries
· Synergistic Integration of Large Models with Emerging AI Technologies
Keywords
Large Language Model, Multimodal, Fine-tuning Technique, Deep Learning, Knowledge Graph, AI, Cross-domain Knowledge
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