mpada's Introduction

MPADA

The implementation of $MP_{ada}$ in Attention-based Multi-patch Aggregation for Image Aesthetic Assessment pdf, the method for SOTA aesthetic visual assessment performance on AVA benchmark. For more comparisons on AVA, please refer to the page on PaperWithCode.

Framework

System overview. We use an attention-based objective to enhance training signals by assigning relatively larger weights to misclassified image patches.

Experiments

Requirements

python == 3.6
tensorflow == 1.2.1
tensorpack == 0.6

Notes

Tensorpack does not implement AVA2012. You need to put the ava2012.py in AVA_info in the folder of tensorpack.dataflow.dataset.
For the information of training and test split of AVA benchmark, please refer to AVA_train.lst and AVA_test.lst in AVA_info.

Instructions for Results in the paper

python AVA2012-resnet_20180808_Revised.py --gpu 2 --data $YOUR_DATA_DIR$/AVA2012
        --aesthetic_level 2 --crop_method_TS RandomCrop --repeat_times 15
        --load $YOUR_CHECKPOINT_DIR$/checkpoint --mode resnet -d 18 --eval

Desired Outputs

TODO

Notes

$YOUR_DATA_DIR$ : The directory you put images of the AVA benchmark.
$YOUR_CHECKPOINT_DIR$ : The directory you save the checkpoint files of the models.
Result might not be reproduced due to several factors: different version of cv2, different CUDA version, different split of training/test.

Citation

Please cite the following paper if you use this repository in your reseach~ Thank you ^ . ^

@inproceedings{sheng2018attention,
  title={Attention-based multi-patch aggregation for image aesthetic assessment},
  author={Sheng, Kekai and Dong, Weiming and Ma, Chongyang and Mei, Xing and Huang, Feiyue and Hu, Bao-Gang},
  booktitle={2018 ACM Multimedia Conference on Multimedia Conference},
  pages={879--886},
  year={2018},
  organization={ACM}
}

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