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      • Faculty of Engineering
      • Department of Computer Engineering
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      Fine-grained object recognition and zero-shot learning in multispectral imagery

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      Author(s)
      Sümbül, Gencer
      Aksoy, Selim
      Cinbiş, R. G.
      Date
      2018
      Source Title
      2018 26th Signal Processing and Communications Applications Conference (SIU)
      Publisher
      IEEE
      Language
      Turkish
      Type
      Conference Paper
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      Abstract
      We present a method for fine-grained object recognition problem, that aims to recognize the type of an object among a large number of sub-categories, and zero-shot learning scenario on multispectral images. In order to establish a relation between seen classes and new unseen classes, a compatibility function between image features extracted from a convolutional neural network and auxiliary information of classes is learnt. Knowledge transfer for unseen classes is carried out by maximizing this function. Performance of the model (15.2%) evaluated with manually annotated attributes, a natural language model, and a scientific taxonomy as auxiliary information is promisingly better than the other methods for 16 test classes.
      Keywords
      Fine-grained classification
      Object recognition
      Zero-shot learning
      Permalink
      http://hdl.handle.net/11693/50215
      Published Version (Please cite this version)
      https://doi.org/10.1109/SIU.2018.8404256
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      • Department of Computer Engineering 1561
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