Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?

@article{Boudiaf2020FewShotSW,
  title={Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?},
  author={Malik Boudiaf and Hoel Kervadec and Imtiaz Masud Ziko and Pablo Piantanida and Ismail Ben Ayed and Jos{\'e} Dolz},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020},
  pages={13974-13983}
}
  • Malik BoudiafH. Kervadec J. Dolz
  • Published 11 December 2020
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
We show that the way inference is performed in few-shot segmentation tasks has a substantial effect on performances—an aspect often overlooked in the literature in favor of the meta-learning paradigm. We introduce a transductive inference for a given query image, leveraging the statistics of its unlabeled pixels, by optimizing a new loss containing three complementary terms: i) the cross-entropy on the labeled support pixels; ii) the Shannon entropy of the posteriors on the unlabeled query… 

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