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Comprehensive Analysis of Graph Representation Learning Techniques and Their Applications in Computer Vision: Trends, Challenges, and Future Directions
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Graph representation learning
computer vision
graph neural networks
graph embedding
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Abstract
Graph representation learning (GRL) has emerged as a pivotal methodology in machine learning, enabling the transformation of graph-structured data into low-dimensional vector spaces while preserving relational and topological information. Its convergence with computer vision (CV) has facilitated advanced modeling of complex visual structures, encompassing scene understanding, object detection, and image segmentation. This paper presents a comprehensive analysis of GRL techniques and their applications in CV, examining prevailing frameworks, optimization strategies, and methodological innovations. We synthesize seminal and recent studies to identify trends, limitations, and research gaps, highlighting the theoretical underpinnings of graph embedding, graph neural networks (GNNs), and graph regularization approaches. Additionally, practical implications are assessed through the lens of model efficiency, interpretability, and scalability. The study culminates in forward-looking insights, emphasizing opportunities for integrating heterogeneous data, improving generalization, and advancing real-world deployment in high-dimensional visual tasks.
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