mlsanityData to 7 Oct 2026

Few-Shot Segmentation with Global and Local Contrastive Learning

arXiv 2108.05293 ↗First posted 11 Aug 2021cs.CVcited by 17

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Abstract (arXiv)

In this work, we address the challenging task of few-shot segmentation. Previous few-shot segmentation methods mainly employ the information of support images as guidance for query image segmentation. Although some works propose to build cross-reference between support and query images, their extraction of query information still depends on the support images. We here propose to extract the information from the query itself independently to benefit the few-shot segmentation task. To this end, we first propose a prior extractor to learn the query information from the unlabeled images with our proposed global-local contrastive learning. Then, we extract a set of predetermined priors via this prior extractor. With the obtained priors, we generate the prior region maps for query images, which locate the objects, as guidance to perform cross interaction with support features. In such a way, the extraction of query information is detached from the support branch, overcoming the limitation by support, and could obtain more informative query clues to achieve better interaction. Without bells and whistles, the proposed approach achieves new state-of-the-art performance for the few-shot segmentation task on PASCAL-5i and COCO datasets.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 28 of its 53 references that are in the map. First posted on arXiv 11 Aug 2021.

Builds on

28 of 53 references.

References · 28

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  3. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  4. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  5. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  6. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  7. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  8. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,282
  9. 2018Unsupervised Feature Learning via Non-Parametric Instance-level Discriminationcited by 1,838
  10. 2018Invariant Information Clustering for Unsupervised Image Classification and Segmentationcited by 454
  11. 2019PANet: Few-Shot Image Semantic Segmentation with Prototype Alignmentcited by 449
  12. 2017One-Shot Learning for Semantic Segmentationcited by 337
16 more
  1. 2020Contrastive learning of global and local features for medical image segmentation with limited annotationscited by 309
  2. 2020Prior Guided Feature Enrichment Network for Few-Shot Segmentationcited by 271
  3. 2019CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learningcited by 217
  4. 2021Adaptive Prototype Learning and Allocation for Few-Shot Segmentationcited by 207
  5. 2021DetCo: Unsupervised Contrastive Learning for Object Detectioncited by 205
  6. 2020Prototype Mixture Models for Few-shot Semantic Segmentationcited by 190
  7. 2019Feature Weighting and Boosting for Few-Shot Segmentationcited by 189
  8. 2018SG-One: Similarity Guidance Network for One-Shot Semantic Segmentationcited by 169
  9. 2020Part-aware Prototype Network for Few-shot Semantic Segmentationcited by 162
  10. 2019FSS-1000: A 1000-Class Dataset for Few-Shot Segmentationcited by 160
  11. 2020Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?cited by 135
  12. 2021Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformercited by 116
  13. 2020CRNet: Cross-Reference Networks for Few-Shot Segmentationcited by 101
  14. 2021Self-Guided and Cross-Guided Learning for Few-Shot Segmentationcited by 92
  15. 2018Coarse-to-fine Semantic Segmentation from Image-level Labelscited by 28
  16. 2019Adaptive Masked Proxies for Few-Shot Segmentationcited by 8

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Cited by year

Citing papers per year: 2021: 4; 2022: 8; 2023: 3; 2024: 2.

17 papers.

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  1. 2022Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentationcited by 93
  2. 2021Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrastcited by 85
  3. 2022Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuningcited by 50
  4. 2021Few-shot Segmentation with Optimal Transport Matching and Message Flowcited by 36
  5. 2023Prototype Adaption and Projection for Few- and Zero-shot 3D Point Cloud Semantic Segmentationcited by 26
  6. 2022Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentationcited by 15
  7. 2021Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentationcited by 13
  8. 2022Self-Regularized Prototypical Network for Few-Shot Semantic Segmentationcited by 12
  9. 2022Improving Few-Shot Part Segmentation using Coarse Supervisioncited by 10
  10. 2021Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuningcited by 8
  11. 2023Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentationcited by 8
  12. 2022CRCNet: Few-shot Segmentation with Cross-Reference and Region-Global Conditional Networkscited by 7
5 more
  1. 2023Integrating Large Pre-trained Models into Multimodal Named Entity Recognition with Evidential Fusioncited by 4
  2. 2022Long-tailed Recognition by Learning from Latent Categoriescited by 3
  3. 2022Spatial Feature Mapping for 6DoF Object Pose Estimationcited by 3
  4. 2024Group-On: Boosting One-Shot Segmentation with Supportive Querycited by 3
  5. 2024Collaborative Position Reasoning Network for Referring Image Segmentationcited by 0