xiaomengyc / SPG

(ECCV2018) Self-produced Guidance for Weakly-supervised Object Localization

Self-produced Guidance for Weakly-supervised Object Localization

We train the SPG model on the ILSVRC dataset, and then apply the trained model on video sequences of DAVIS 2016.

Overview of SPG

Train

We finetune the SPG model on the ILSVRC dataset.

cd scripts
sh train_imagenet_full_v5.sh

Test

Download the pretrined model at GoogleDrive(https://drive.google.com/open?id=1EwRuqfGASarGidutnYB8rXLSuzYpEoSM (IMAGENET),https://drive.google.com/open?id=1WfrELBlEoq5WO7gKUv-MLTQ8QHY-2wiX (CUB)).

Use the test script to generate attention maps.

cd scripts
sh val_imagenet_full.sh

Demo

Thanks to Jun Hao for providing the wonderful demos!

Please see the setup_demo.txt for more guidance of setuping up the demos.

Masks are getting better with the proposed easy-to-hard approach.

Citation

If you find this code helpful, please consider to cite this paper:

@inproceedings{zhang2018self,
  title={Self-produced Guidance for Weakly-supervised Object Localization},
  author={Zhang, Xiaolin and Wei, Yunchao and Kang, Guoliang and Yang, Yi and Huang, Thomas},
  booktitle={European Conference on Computer Vision},
  year={2018},
  organization={Springer}
}