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      Attributes2Classname: a discriminative model for attribute-based unsupervised zero-shot learning

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      Author(s)
      Demirel, B.
      Cinbiş, Ramazan Gökberk
      İkizler-Cinbiş, N.
      Date
      2017-10
      Source Title
      Proceedings of the IEEE International Conference on Computer Vision
      Publisher
      IEEE
      Pages
      1241 - 1250
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      We propose a novel approach for unsupervised zero-shot learning (ZSL) of classes based on their names. Most existing unsupervised ZSL methods aim to learn a model for directly comparing image features and class names. However, this proves to be a difficult task due to dominance of non-visual semantics in underlying vector-space embeddings of class names. To address this issue, we discriminatively learn a word representation such that the similarities between class and combination of attribute names fall in line with the visual similarity. Contrary to the traditional zero-shot learning approaches that are built upon attribute presence, our approach bypasses the laborious attributeclass relation annotations for unseen classes. In addition, our proposed approach renders text-only training possible, hence, the training can be augmented without the need to collect additional image data. The experimental results show that our method yields state-of-the-art results for unsupervised ZSL in three benchmark datasets. © 2017 IEEE.
      Keywords
      Semantics
      Vector spaces
      Attribute-based
      Benchmark datasets
      Discriminative models
      Image features
      Learning approach
      State of the art
      Visual similarity
      Word representations
      Computer vision
      Permalink
      http://hdl.handle.net/11693/37629
      Published Version (Please cite this version)
      https://doi.org/10.1109/ICCV.2017.139
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      • Department of Computer Engineering 1561
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