Learning Hierarchical Graph Neural Networks for Image Clustering

• URL: http://arxiv.org/abs/2107.01319v1
• Date: Sat, 3 Jul 2021 01:28:42 GMT
• Title: Learning Hierarchical Graph Neural Networks for Image Clustering
• Authors: Yifan Xing, Tong He, Tianjun Xiao, Yongxin Wang, Yuanjun Xiong, Wei Xia, David Wipf Paul, Zheng Zhang, Stefano Soatto
• Abstract summary: We propose a hierarchical graph neural network (GNN) model that learns how to cluster a set of images into an unknown number of identities. Our hierarchical GNN uses a novel approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level.
• Score: 81.5841862489509
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a hierarchical graph neural network (GNN) model that learns how to cluster a set of images into an unknown number of identities using a training set of images annotated with labels belonging to a disjoint set of identities. Our hierarchical GNN uses a novel approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level. Unlike fully unsupervised hierarchical clustering, the choice of grouping and complexity criteria stems naturally from supervision in the training set. The resulting method, Hi-LANDER, achieves an average of 54% improvement in F-score and 8% increase in Normalized Mutual Information (NMI) relative to current GNN-based clustering algorithms. Additionally, state-of-the-art GNN-based methods rely on separate models to predict linkage probabilities and node densities as intermediate steps of the clustering process. In contrast, our unified framework achieves a seven-fold decrease in computational cost. We release our training and inference code at https://github.com/dmlc/dgl/tree/master/examples/pytorch/hilander.

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