Few-shot segmentation based on multi-level and cross-scale clustering

Abstract

The problem of image segmentation with few-shot learning is addressed in this paper, which is a challenging task due to the lack of sufficient high-precision annotated data. A novel method that consists of two modules is proposed: a multi-level fuzzy clustering guidance module and a cross-scale feature fusion module. The former module can extract image features in a class-independent feature space and fuse them with different scale information, while the latter module can reduce the information loss caused by cross-scale transmission. The feature association map between the support image and the query image can be learned by the proposed method, and the inconsistency of target object categories can be overcome. The proposed method is evaluated on Pascal and COCO datasets, and it is shown that it outperforms the state-of-the-art algorithms in both one-shot and k-shot segmentation scenarios.

KEYWORDS:

• Few-shot segmentation
• fuzzy clustering
• multi-level
• cross-scale

Acknowledgements

The authors also gratefully acknowledge the reviewers’ helpful comments and suggestions, which will improve the presentation significantly.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was funded by National Natural Science Foundation of China NSF of China [grant numbers 62007017, 61873117, U22A2033, 62171209, 62176140] and Basic Research Project of Yantai Science and Technology Innovation Development Plan (2023JCYJ044).