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.
Requires: Keras 1.2.1 ("image_data_format": "channels_first")
Run in two steps:
$CUB
; Copy file tools/processed_attributes.txt
to $CUB
.$CUB
dir should be like this:
data_dir
in run.sh
to $CUB
, run the scprit sh run.sh
to obtain the result.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}
}