Hi-LANDER

Introduced by Xing et al. in Learning Hierarchical Graph Neural Networks for Image Clustering

Hi-LANDER is a hierarchical graph neural network (GNN) model that learns how to cluster a set of images into an unknown number of identities using an image annotated with labels belonging to a disjoint set of identities. The hierarchical GNN uses an 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.

Source: Learning Hierarchical Graph Neural Networks for Image Clustering

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Papers

Paper	Code	Results	Date	Stars

Tasks

Task	Papers	Share
Clustering	1	33.33%
Face Clustering	1	33.33%
Image Clustering	1	33.33%

Usage Over Time

This feature is experimental; we are continuously improving our matching algorithm.

Components

Component	Type	Add Remove
🤖 No Components Found	You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

Categories

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Graph Models