Learning Hierarchical Graph Neural Networks for Image Clustering

Published in IEEE Conference on Computer Vision (ICCV), 2021

Y Xing, T He, T Xiao, Y Wang, Y Xiong, W Xia, D Wipf, Z Zhang, S Soatto. In Proceedings of the IEEE conference on computer vision. ICCV 2021


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, stateof-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 here.


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