# CRCNet: Few-shot Segmentation with Cross-Reference and Region-Global Conditional Networks Source: [CRCNet: Few-shot Segmentation with Cross-Reference and Region-Global Conditional Networks]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:31:24.160Z Authors: Weide Liu, Chi Zhang, Guosheng Lin, Fayao Liu Published: 2022-08-23T06:46:18.000Z ## 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. ## 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]()