论文标题
clustergnn：基于群集的粗到五个图形神经网络，用于有效的特征匹配
ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matching
论文作者
论文摘要
图形神经网络（GNN）已成功地用于学习视觉功能匹配。但是，当前方法通过完整的图学习学习，从而导致功能数量的二次复杂性。通过先前的观察，即自我和交叉注意矩阵融合到稀疏表示形式时，我们提出了clustergnn，这是一种注意力集GNN体系结构，该体系结构在群集上运行，用于学习功能匹配任务。使用渐进式聚类模块，我们将关键点自适应地分为不同的子图，以降低冗余连通性，并采用粗到细节的范式来减轻图像中的错过分类。与当前的基于GNN的最新匹配相比，我们的方法的运行时间降低了59.7％，而密集检测的记忆消耗降低了58.4％，同时在各种计算机视觉任务上取得了竞争性能。
Graph Neural Networks (GNNs) with attention have been successfully applied for learning visual feature matching. However, current methods learn with complete graphs, resulting in a quadratic complexity in the number of features. Motivated by a prior observation that self- and cross- attention matrices converge to a sparse representation, we propose ClusterGNN, an attentional GNN architecture which operates on clusters for learning the feature matching task. Using a progressive clustering module we adaptively divide keypoints into different subgraphs to reduce redundant connectivity, and employ a coarse-to-fine paradigm for mitigating miss-classification within images. Our approach yields a 59.7% reduction in runtime and 58.4% reduction in memory consumption for dense detection, compared to current state-of-the-art GNN-based matching, while achieving a competitive performance on various computer vision tasks.
