  Recent developments in self-supervised learning (SSL) have made it possible
to learn data representations without the need for annotations. Inspired by the
non-contrastive SSL approach (SimSiam), we introduce a novel framework SIMSAM
to compute the Semantic Affinity Matrix, which is significant for unsupervised
image segmentation. Given an image, SIMSAM first extracts features using
pre-trained DINO-ViT, then projects the features to predict the correlations of
dense features in a non-contrastive way. We show applications of the Semantic
Affinity Matrix in object segmentation and semantic segmentation tasks. Our
code is available at https://github.com/chandagrover/SimSAM.
