ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matching

• URL: http://arxiv.org/abs/2204.11700v1
• Date: Mon, 25 Apr 2022 14:43:15 GMT
• Title: ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matching
• Authors: Yan Shi, Jun-Xiong Cai, Yoli Shavit, Tai-Jiang Mu, Wensen Feng and Kai Zhang
• Abstract summary: ClusterGNN is an attentional GNN architecture which operates on clusters for learning the feature matching task. Our approach yields a 59.7% reduction in runtime and 58.4% reduction in memory consumption for dense detection.
• Score: 15.620335576962475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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