Guest Editor(s)
Assoc. Prof. Dr. Yonis Gulzar
Email: ygulzar@kfu.edu.sa
Affiliation: Department of Management Information Systems, King Faisal University, Al-Ahsa, Saudi Arabia
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Research Interests: deep learning, computer vision, image recognition, medical image analysis, agricultural vision intelligence, explainable artificial intelligence, vision transformers, multimodal learning, pattern recognition, intelligent visual computing
Assoc. Prof. Dr. Uzair Aslam Bhatti
Email: uzair@hainanu.edu.cn
Affiliation: Department of Artificial Intelligence, School of Information and Communication Engineering, Hainan University, Haikou, China
Homepage:
Research Interests: computer vision, deep learning, pattern recognition, image analysis, vision transformers, multimodal artificial intelligence, explainable AI, intelligent visual systems, visual data analytics, machine learning applications
Summary
Recent breakthroughs in deep learning have significantly advanced the capabilities of image recognition and visual intelligence systems, driving innovation across autonomous transportation, agriculture, industrial automation, security, and smart city applications. Emerging paradigms such as vision transformers, foundation models, multimodal learning, and explainable artificial intelligence are reshaping the way machines perceive, interpret, and interact with visual information. Despite remarkable progress, challenges related to model transparency, computational efficiency, robustness, data limitations, and real-world deployment continue to motivate further research.
This Special Issue aims to provide an interdisciplinary platform for disseminating cutting-edge research and comprehensive reviews on advanced deep learning techniques for image recognition and visual intelligence. The issue seeks to highlight novel algorithms, architectures, and applications that contribute to the development of reliable, efficient, and intelligent visual computing systems.
The objectives of this Special Issue are:
· To explore recent advances in deep learning methodologies for image recognition and visual intelligence.
· To present innovative architectures, algorithms, and frameworks that improve the accuracy, efficiency, robustness, and interpretability of computer vision systems.
· To showcase emerging applications of deep learning-based image analysis across domains such as agriculture, autonomous systems, industrial automation, security, and smart cities.
· To address current challenges related to data quality, computational complexity, scalability, explainability, and real-world deployment of intelligent visual computing solutions.
· To promote interdisciplinary collaboration and knowledge exchange among researchers and practitioners working in image recognition, computer vision, and artificial intelligence.
Keywords
deep learning, image recognition, computer vision, visual intelligence, vision transformers, object detection, explainable artificial intelligence, multimodal learning, foundation models, image segmentation
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