Recent developments in self-supervised learning (SSL) have made it possible to learn data representations without the need for annotations.
, which enhances the performance of various downstream tasks.
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://anonymous.4open.science/r/SimSAM-E746/.