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