CRCNet: Few-Shot Segmentation with Cross-Reference and Region–Global Conditional Networks

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CRCNet: Few-Shot Segmentation with Cross-Reference and Region–Global Conditional Networks
Title:
CRCNet: Few-Shot Segmentation with Cross-Reference and Region–Global Conditional Networks
Journal Title:
International Journal of Computer Vision
Publication Date:
30 September 2022
Citation:
Liu, W., Zhang, C., Lin, G., & Liu, F. (2022). CRCNet: Few-Shot Segmentation with Cross-Reference and Region–Global Conditional Networks. International Journal of Computer Vision, 130(12), 3140–3157. https://doi.org/10.1007/s11263-022-01677-7
Abstract:
Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images. In this paper, we propose a Cross-Reference and Local–Global Conditional Networks (CRCNet) for few-shot segmentation. Unlike previous works that only predict the query image’s mask, our proposed model concurrently makes predictions for both the support image and the query image. Our network can better find the co-occurrent objects in the two images with a cross-reference mechanism, thus helping the few-shot segmentation task. To further improve feature comparison, we develop a local-global conditional module to capture both global and local relations. We also develop a mask refinement module to refine the prediction of the foreground regions recurrently. Experiments on the PASCAL VOC 2012, MS COCO, and FSS-1000 datasets show that our network achieves new state-of-the-art performance.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - AME Programmatic Funds
Grant Reference no. : A20H6b0151

This research / project is supported by the National Research Foundation, Singapore - AI Singapore Programme
Grant Reference no. : AISG-RP-2018-003

This research / project is supported by the Ministry of Education - Academic Research Fund Tier 2
Grant Reference no. : MOE-T2EP20220-0007

This research / project is supported by the Ministry of Education - Academic Research Fund Tier 1
Grant Reference no. : RG95/20
Description:
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11263-022-01677-7
ISSN:
1573-1405
0920-5691
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