论文标题
计算机视觉中的图形神经网络 - 体系结构，数据集和通用方法
Graph Neural Networks in Computer Vision -- Architectures, Datasets and Common Approaches
论文作者
论文摘要
图形神经网络（GNN）是一个受图表上的节点之间存在的机制启发的图形网络家族。近年来，对GNN及其衍生物的兴趣增加了，即图形注意网络（GAT），图形卷积网络（GCN）和图形复发网络（GRN）。还观察到它们在计算机视觉方面的可用性也有所提高。该领域的GNN应用程序数量继续扩大；它包括视频分析和理解，行动和行为识别，计算摄影，图像和视频综合，来自零或几镜头等等。这项贡献旨在收集有关基于GNN的计算机视觉方法的论文。它们是从三个角度描述和总结的。首先，我们研究了图形神经网络及其在该领域使用的衍生物的架构，以为随后的研究提供准确且可解释的建议。至于另一方面，我们还介绍了这些作品中使用的数据集。最后，使用图形分析，我们还研究了基于GNN的计算机视觉研究与在该领域外确定的潜在灵感来源之间的关系。
Graph Neural Networks (GNNs) are a family of graph networks inspired by mechanisms existing between nodes on a graph. In recent years 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. The number of GNN applications in this field 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 from three perspectives. Firstly, we investigate the architectures of Graph Neural Networks and their derivatives used in this area to provide accurate and explainable recommendations for the ensuing investigations. As for the other aspect, we also present datasets used in these works. Finally, using graph analysis, we also examine relations between GNN-based studies in computer vision and potential sources of inspiration identified outside of this field.
