Revisiting Local Descriptor Based Image-To-Class Measure for Few-Shot Learning

@article{Li2019RevisitingLD,
  title={Revisiting Local Descriptor Based Image-To-Class Measure for Few-Shot Learning},
  author={Wenbin Li and Lei Wang and J. Xu and Jing Huo and Y. Gao and Jiebo Luo},
  journal={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019},
  pages={7253-7260}
}
  • Wenbin Li, Lei Wang, +3 authors Jiebo Luo
  • Published 2019
  • Computer Science
  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Few-shot learning in image classification aims to learn a classifier to classify images when only few training examples are available for each class. Recent work has achieved promising classification performance, where an image-level feature based measure is usually used. In this paper, we argue that a measure at such a level may not be effective enough in light of the scarcity of examples in few-shot learning. Instead, we think a local descriptor based image-to-class measure should be taken… CONTINUE READING
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