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SimSAM: Simple Siamese Representations Based Semantic Affinity Matrix for Unsupervised Image Segmentation

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Citation Author(s):
Chanda Grover Kamra, Indra Deep Mastan, Debayan Gupta
Submitted by:
Chanda Grover Kamra
Last updated:
8 February 2024 - 3:06am
Document Type:
Supplementary Material
Document Year:
2024
Presenters:
Chanda Grover Kamra
Paper Code:
1183
 

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/.

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