Graph neural networks and distributed learning have emerged as powerful techniques for solving complex problems in computer vision. However, despite significant progress in both fields, there is still a gap between theoretical research and practical applications. This Research Topic aims to explore recent developments in the fields of graph neural networks and distributed learning and their applications in computer vision.
The topic will seek submissions covering themes such as novel architectures for graph neural networks, techniques for distributed learning in computer vision, and the practical applications of these techniques in real-world scenarios like autonomous driving, surveillance, and medical image analysis. The Research Topic will also include papers that explore theoretical aspects of graph neural networks and distributed learning and empirical evaluations of their performance compared to traditional deep learning models.
The goal of this Research Topic is to bring together researchers and practitioners from the fields of computer vision, graph neural networks, and distributed learning to share their latest insights, applications, and experiences.
Topics of interest include, but are not limited to:
• Advancements in graph neural network architectures for computer vision tasks such as image classification, object detection, and semantic segmentation.
• Novel approaches to distributed learning in computer vision, including federated learning, multi-agent learning, and decentralized learning.
• Applications of graph neural networks and distributed learning to real-world computer vision problems such as autonomous driving, surveillance, and medical image analysis.
• Theoretical analysis and empirical evaluation of graph neural networks and distributed learning in computer vision, including comparisons with traditional deep learning models.
• Techniques for improving the scalability, robustness, and interpretability of graph neural networks and distributed learning in computer vision.
• Challenges and future directions in graph neural networks and distributed learning for computer vision, including ethical and societal considerations.
Keywords:
Deep learning, Distributed Learning, Graph Neural Networks, IoT, Intelligent edge and Edge computing for IoT and Sensors, Data analytics for IoT and Sensors
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Graph neural networks and distributed learning have emerged as powerful techniques for solving complex problems in computer vision. However, despite significant progress in both fields, there is still a gap between theoretical research and practical applications. This Research Topic aims to explore recent developments in the fields of graph neural networks and distributed learning and their applications in computer vision.
The topic will seek submissions covering themes such as novel architectures for graph neural networks, techniques for distributed learning in computer vision, and the practical applications of these techniques in real-world scenarios like autonomous driving, surveillance, and medical image analysis. The Research Topic will also include papers that explore theoretical aspects of graph neural networks and distributed learning and empirical evaluations of their performance compared to traditional deep learning models.
The goal of this Research Topic is to bring together researchers and practitioners from the fields of computer vision, graph neural networks, and distributed learning to share their latest insights, applications, and experiences.
Topics of interest include, but are not limited to:
• Advancements in graph neural network architectures for computer vision tasks such as image classification, object detection, and semantic segmentation.
• Novel approaches to distributed learning in computer vision, including federated learning, multi-agent learning, and decentralized learning.
• Applications of graph neural networks and distributed learning to real-world computer vision problems such as autonomous driving, surveillance, and medical image analysis.
• Theoretical analysis and empirical evaluation of graph neural networks and distributed learning in computer vision, including comparisons with traditional deep learning models.
• Techniques for improving the scalability, robustness, and interpretability of graph neural networks and distributed learning in computer vision.
• Challenges and future directions in graph neural networks and distributed learning for computer vision, including ethical and societal considerations.
Keywords:
Deep learning, Distributed Learning, Graph Neural Networks, IoT, Intelligent edge and Edge computing for IoT and Sensors, Data analytics for IoT and Sensors
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.