Extreme weakly supervised binary semantic image segmentation via one-pixel supervision

• Binary segmentation with sparse one-pixel annotations, even a single one per dataset. • Our method operates without requiring background annotations. • Novel contrastive loss using class-of-interest one-pixel annotations. • Dynamic contrastive loss hyperparameter computation based on image features. Despite recent advancements, Unsupervised Semantic Segmentation (USS) methods still exhibit a significant performance deficit compared to supervised approaches, particularly in binary semantic segmentation. This limitation arises because, without supervision, USS methods struggle to distinguish foreground from background image regions, particularly when the foreground contains small or uncommon objects. This issue is addressed by our proposed Extremely Weakly Supervised Binary Semantic Segmentation (EWS) framework. EWS expects minimal supervision, consisting only of a small set of one-pixel annotations explicitly belonging to the foreground class across the entire image dataset. Our approach leverages these one-pixel annotations and employs two contrastive losses to map visual transformer features into well-separated foreground and background feature clusters. Additionally, we propose a novel loss function to eliminate the need for hyperparameter tuning of the contrastive loss threshold, by dynamically computing it based on the similarity between the input image features. Even if we employ a single one-pixel annotation, EWS achieves competitive results in binary segmentation tasks while maintaining low computational costs, making it an efficient solution for critical segmentation applications. GitHub Repo: https://github.com/matJTzimas/EWS