mlsanityData to 14 Oct 2026

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

arXiv 2208.10761 ↗First posted 23 Aug 2022cs.CVcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 48 of its 77 references that are in the map. First posted on arXiv 23 Aug 2022.

Builds on

48 of 77 references.

References · 48

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36 more
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  12. 2019Feature Weighting and Boosting for Few-Shot Segmentationcited by 189
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  21. 2021Mining Latent Classes for Few-shot Segmentationcited by 69
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Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 4; 2024: 2.

7 papers.

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  2. 2022Hierarchical Normalization for Robust Monocular Depth Estimationcited by 52
  3. 2024Networking Systems for Video Anomaly Detection: A Tutorial and Surveycited by 21
  4. 2023LCReg: Long-Tailed Image Classification with Latent Categories based Recognitioncited by 9
  5. 2023Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentationcited by 8
  6. 2023Integrating Large Pre-trained Models into Multimodal Named Entity Recognition with Evidential Fusioncited by 4
  7. 2024Symmetrical Joint Learning Support-query Prototypes for Few-shot Segmentationcited by 0