Graph Models

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


Paper Code Results Date Stars


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


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