A Survey of Zero-Shot Learning: Settings, Methods, and Applications

@article{Wang2019ASO,
  title={A Survey of Zero-Shot Learning: Settings, Methods, and Applications},
  author={Wei Wang and Vincent Wenchen Zheng and Han Yu and Chunyan Miao},
  journal={ACM TIST},
  year={2019},
  volume={10},
  pages={13:1-13:37}
}
Most machine-learning methods focus on classifying instances whose classes have already been seen in training. In practice, many applications require classifying instances whose classes have not been seen previously. Zero-shot learning is a powerful and promising learning paradigm, in which the classes covered by training instances and the classes we aim to classify are disjoint. In this paper, we provide a comprehensive survey of zero-shot learning. First of all, we provide an overview of zero… CONTINUE READING

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  • 2019
VIEW 2 EXCERPTS
CITES BACKGROUND

References

Publications referenced by this paper.
SHOWING 1-10 OF 22 REFERENCES

Matrix Tri-Factorization with Manifold Regularizations for Zero-Shot Learning

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Zero-Shot Learning — The Good, the Bad and the Ugly

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017
VIEW 7 EXCERPTS
HIGHLY INFLUENTIAL

Zero-Shot Visual Recognition via Bidirectional Latent Embedding

  • International Journal of Computer Vision
  • 2017
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Learning to detect unseen object classes by between-class attribute transfer

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition
  • 2009
VIEW 9 EXCERPTS
HIGHLY INFLUENTIAL

From Zero-Shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

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