Fine-Grained Recognition as HSnet Search for Informative Image Parts

@article{Lam2017FineGrainedRA,
  title={Fine-Grained Recognition as HSnet Search for Informative Image Parts},
  author={Michael Lam and Behrooz Mahasseni and Sinisa Todorovic},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={6497-6506}
}
This work addresses fine-grained image classification. Our work is based on the hypothesis that when dealing with subtle differences among object classes it is critical to identify and only account for a few informative image parts, as the remaining image context may not only be uninformative but may also hurt recognition. This motivates us to formulate our problem as a sequential search for informative parts over a deep feature map produced by a deep Convolutional Neural Network (CNN). A state… CONTINUE READING
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