# MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation Source: [MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:41:15.837Z Authors: Mustafa Sercan Amac, Ahmet Sencan, Orhun Bugra Baran, Nazli Ikizler-Cinbis, Ramazan Gokberk Cinbis Published: 2021-10-23T12:30:05.000Z ## Abstract Just like other few-shot learning problems, few-shot segmentation aims to minimize the need for manual annotation, which is particularly costly in segmentation tasks. Even though the few-shot setting reduces this cost for novel test classes, there is still a need to annotate the training data. To alleviate this need, we propose a self-supervised training approach for learning few-shot segmentation models. We first use unsupervised saliency estimation to obtain pseudo-masks on images. We then train a simple prototype based model over different splits of pseudo masks and augmentations of images. Our extensive experiments show that the proposed approach achieves promising results, highlighting the potential of self-supervised training. To the best of our knowledge this is the first work that addresses unsupervised few-shot segmentation problem on natural images. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()