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Attribute-Aware Attention Model

Code for ACM Multimedia 2018 oral paper: Attribute-Aware Attention Model for Fine-grained Representation Learning

We have presented results of fine-grained classification, person re-id, image retrieval tasks, including CUB-200-2011, Market-1501, CARS196 datasets in the paper. Here is the example of fine-grained classification. For detailed results, refer to the original paper or ArXiv.

Usage

Requires: Keras 1.2.1 ("image_data_format": "channels_first")

Run in two steps:

  1. Download CUB-200-2011 dataset here and unzip it to $CUB; Copy file tools/processed_attributes.txt to $CUB.
  • The $CUB dir should be like this:
  1. Change data_dir in run.sh to $CUB, run the scprit sh run.sh to obtain the result.
  • Result on CUB dataset

Citation

Please use the following bibtex to cite our work:

@inproceedings{han2018attribute,
  title={Attribute-Aware Attention Model for Fine-grained Representation Learning},
  author={Han, Kai and Guo, Jianyuan and Zhang, Chao and Zhu, Mingjian},
  booktitle={Proceedings of the 26th ACM international conference on Multimedia},
  pages={2040--2048},
  year={2018},
  organization={ACM}
}

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