MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation

Amac, Mustafa Sercan
Sencan, Ahmet
Baran, Orhun Buğra
Ikizler-Cinbis, Nazli
Cinbiş, Ramazan Gökberk
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.
22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022


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Citation Formats
M. S. Amac, A. Sencan, O. B. Baran, N. Ikizler-Cinbis, and R. G. Cinbiş, “MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation,” presented at the 22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022, Hawaii, Amerika Birleşik Devletleri, 2022, Accessed: 00, 2022. [Online]. Available: