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Semi-Supervised Few-Shot Segmentation with Noisy Support Images

  • Published:2023-10-08 Issue: Volume: Page:
  • ISSN:
  • Container-title:2023 IEEE International Conference on Image Processing (ICIP)
  • language:
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Author:

Zhang Runtong1,Zhu Hongyuan2,Zhang Hanwang3,Gong Chen4,Zhou Joey Tianyi5,Meng Fanman1

Affiliation:

1. University of Electronic Science and Technology of China,Chengdu,China

2. Institute for Infocomm Research (I2R) & Centre for Frontier AI Research (CFAR),A*STAR,Singapore

3. Nanyang Technological University,Singapore

4. Nanjing University of Science and Technology,Nanjing,China

5. Centre for Frontier AI Research (CFAR),A*STAR,Singapore

Funder

National Natural Science Foundation of China

Natural Science Foundation of Sichuan Province

Technology Development

Natural Science Foundation of Jiangsu Province

Publisher

IEEE

Reference20 articles.

1. Dividemix: Learning with noisy labels as semi-supervised learning;li;International Conference on Learning Representations,2019

2. Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation

3. Feature Weighting and Boosting for Few-Shot Segmentation

4. Co-teaching: Robust training of deep neural networks with extremely noisy labels;han;Neural Information Processing Systems,2018

5. Integrative Few-Shot Learning for Classification and Segmentation

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