Guest Editors
Dr. Bowen Wang
Email: wang@ids.osaka-u.ac.jp
Affiliation: D3 Center, Osaka University, Suita, 565-0871, Japan
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Research Interests: Computer Vision, Large Vision Language Models, Explainable AI, Medical AI, Natural Language Processing
Dr. Jiaxin Zhang
Email: jiaxin.arch@ncu.edu.cn
Affiliation: Architecture and Design College, Nanchang University
Homepage:
Research Interests: AI Agent, Urban perception, Machine Learning
Summary
Recently Large Vision Language Models (LVLMs) have made significant advances in the intersection of computer vision and natural language processing. By leveraging multimodal integration, LVLMs have achieved outstanding understanding and reasoning capabilities between images and text, demonstrating outstanding performance in tasks such as visual question answering, image generation, text description, etc. However, as the real-world applications of LVLMs continue to expand, several key issues have emerged, including model interpretability, reliability, fairness, and high computational resource requirements. This special issue aims to bring together the latest research findings to explore the development of LVLMs in terms of theoretical innovations, practical applications, and diverse challenges. We invite scholars and practitioners to share their research, encompassing topics from model architecture optimization and training method improvements to real-world deployment and impact analysis in fields such as healthcare, autonomous driving, and smart cities. The target of this special issue is to advance the broad application and further exploration of LVLMs in the realm of multimodal artificial intelligence.
The topic includes but is not limited to:
Model architecture optimization for efficiency and performance
Advanced training techniques and large-scale model fine-tuning
Interpretability and explainability of vision-language models
Fairness and bias mitigation in multimodal AI systems
Energy-efficient algorithms and sustainable deployment strategies
Practical case studies and applications in diverse fields
Ethical considerations and societal impact analysis
Integration of LVLMs with other emerging AI technologies
LVLMs as an AI agent
Keywords
Large Vision Language Models, Computer Vision, Deep Learning, Vision Language Question, Image Caption, Interpretability, Reliability, Real-World Deployment, Multimodal Integration, Computational Efficiency, AI Agent, Bias Mitigation, Cross-Modal Reasoning
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