Survey of graph neural networks in computer vision with architectures, datasets, common approaches
Survey of graph neural networks in computer vision with architectures, datasets, common approaches
Graph Neural Networks in Computer Vision -- Architectures, Datasets and Common Approaches
arXiv paper abstract https://arxiv.org/abs/2212.10207
arXiv PDF paper https://arxiv.org/pdf/2212.10207.pdf
Graph Neural Networks (GNNs) are a family of graph networks inspired by mechanisms existing between nodes on a graph.
... there has been an increased interest in GNN and their derivatives, i.e., Graph Attention Networks (GAT), Graph Convolutional Networks (GCN), and Graph Recurrent Networks (GRN). An increase in their usability in computer vision is also observed.
... GNN applications ... continues to expand; it includes video analysis and understanding, action and behavior recognition, computational photography, image and video synthesis from zero or few shots, and many more.
This contribution aims to collect papers published about GNN-based approaches towards computer vision. They are described and summarized
... investigate the architectures of Graph Neural Networks and their derivatives used in this area to provide accurate and explainable recommendations for the ensuing investigations ... also present datasets used in these works.
... also examine relations between GNN-based studies in computer vision and potential sources of inspiration identified outside of this field.
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