[ACM MM 2018] Attribute-Aware Attention Model for Fine-grained Representation Learning
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 ("imagedataformat": "channels_first")
Run in two steps:
$CUB; Copy file
tools/processed_attributes.txtto
$CUB.
$CUBdir should be like this:
data_dirin
run.shto
$CUB, run the scprit
sh run.shto 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} }