Transductive Visual-Semantic Embedding for Zero-shot Learning

@inproceedings{Xu2017TransductiveVE,
  title={Transductive Visual-Semantic Embedding for Zero-shot Learning},
  author={Xing Xu and Fumin Shen and Yang Yang and Jie Shao and Zi Xuan Huang},
  booktitle={ICMR},
  year={2017}
}
Zero-shot learning (ZSL) aims to bridge the knowledge transfer via available semantic representations (e.g., attributes) between labeled source instances of seen classes and unlabelled target instances of unseen classes. Most existing ZSL approaches achieve this by learning a projection from the visual feature space to the semantic representation space based on the source instances, and directly applying it to the target instances. However, the intrinsic manifold structures residing in both… CONTINUE READING

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References

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

Synthesized Classifiers for Zero-Shot Learning

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
VIEW 9 EXCERPTS
HIGHLY INFLUENTIAL

Zero-Shot Learning via Joint Latent Similarity Embedding

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2016
VIEW 11 EXCERPTS
HIGHLY INFLUENTIAL

Transductive Multi-View Zero-Shot Learning

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2015
VIEW 9 EXCERPTS
HIGHLY INFLUENTIAL

Zero-Shot Learning via Semantic Similarity Embedding

  • 2015 IEEE International Conference on Computer Vision (ICCV)
  • 2015
VIEW 8 EXCERPTS
HIGHLY INFLUENTIAL

Label-Embedding for Attribute-Based Classification

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition
  • 2013
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Transfer Learning with Graph Co-Regularization

  • IEEE Transactions on Knowledge and Data Engineering
  • 2012
VIEW 3 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 6 EXCERPTS
HIGHLY INFLUENTIAL

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