Zero-Shot Learning via Joint Latent Similarity Embedding

@article{Zhang2016ZeroShotLV,
  title={Zero-Shot Learning via Joint Latent Similarity Embedding},
  author={Ziming Zhang and Venkatesh Saligrama},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={6034-6042}
}
Zero-shot recognition (ZSR) deals with the problem of predicting class labels for target domain instances based on source domain side information (e.g. attributes) of unseen classes. We formulate ZSR as a binary prediction problem. Our resulting classifier is class-independent. It takes an arbitrary pair of source and target domain instances as input and predicts whether or not they come from the same class, i.e. whether there is a match. We model the posterior probability of a match since it… CONTINUE READING

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Key Quantitative Results

  • We also adapt ZSR method for zero-shot retrieval and show 22.45% improvement accordingly in mean average precision (mAP).

Citations

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  • 19% Increase in citations per year in 2018 over 2017

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