SimSAM: Simple Siamese Representations Based Semantic Affinity Matrix for Unsupervised Image Segmentation
<|reference_start|>SimSAM: Simple Siamese Representations Based Semantic Affinity Matrix for Unsupervised Image Segmentation: 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.<|reference_end|>
@article{kamra2024simsam:,
    title={SimSAM: Simple Siamese Representations Based Semantic Affinity Matrix
  for Unsupervised Image Segmentation},
    author={Chanda Grover Kamra, Indra Deep Mastan, Nitin Kumar, Debayan Gupta},
    journal={ICIP 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07986},
    primaryClass={cs.CV}
}