Guest Editor(s)
Dr. Yang Li
Email: liyang328@shzu.edu.cn
Affiliation: College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China
Homepage:
Research Interests: information processing, artificial intelligence
Dr. Shuo Yang
Email: syang1@xjau.edu.cn
Affiliation: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi, China
Homepage:
Research Interests: generative AI, pattern recognition
Summary
Generative AI is fundamentally reshaping computer vision by enabling systems to not only perceive but also synthesize, reason about, and interact with visual data, driving a paradigm shift from discriminative recognition toward unified visual understanding and generation. This convergence is catalyzing a new class of intelligent systems whose potential impact spans industrial inspection, autonomous navigation, and human–computer interaction.
This Special Issue aims to consolidate cutting-edge research at the intersection of generative AI and computer vision-based intelligent systems. We seek original contributions that advance generative architectures (diffusion models, GANs, vision-language models) for visual understanding, synthesis, and decision-making, and that demonstrate their integration into real-world intelligent systems. The scope encompasses theoretical foundations, algorithmic innovations, trustworthy deployment, and domain-specific applications. We particularly encourage work addressing multimodal reasoning, retrieval-augmented generation for vision, efficiency and robustness in deployment, and the ethical and explainability dimensions of generative visual intelligence.
Suggested themes
· Synthetic data generation and data-centric learning
· Efficient and deployable generative vision applications
· Generative AI for domain-specific intelligent systems
· Vision-language models and multimodal reasoning
· Retrieval-augmented generation in computer vision
Keywords
generative AI, computer vision, pattern recognition, multimodal, synthetic
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