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Image segmentation is a fundamental task in computer vision. Data annotationfor training supervised methods can be labor-intensive, motivating unsupervisedmethods. Current approaches often rely on extracting deep features frompre-trained networks to construct a graph, and classical clustering methodslike k-means and normalized-cuts are then applied as a post-processing step.However, this approach reduces the high-dimensional information encoded in thefeatures to pair-wise scalar affinities. To address this limitation, this studyintroduces a lightweight Graph Neural Network (GNN) to replace classicalclustering methods while optimizing for the same clustering objective function.Unlike existing methods, our GNN takes both the pair-wise affinities betweenlocal image features and the raw features as input. This direct connectionbetween the raw features and the clustering objective enables us to implicitlyperform classification of the clusters between different graphs, resulting inpart semantic segmentation without the need for additional post-processingsteps. We demonstrate how classical clustering objectives can be formulated asself-supervised loss functions for training an image segmentation GNN.Furthermore, we employ the Correlation-Clustering (CC) objective to performclustering without defining the number of clusters, allowing for k-lessclustering. We apply the proposed method for object localization, segmentation,and semantic part segmentation tasks, surpassing state-of-the-art performanceon multiple benchmarks.

DeepCut: Unsupervised Segmentation using Graph Neural Networks Clustering