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MRNet - Multi-scale Reasoning Network

Official repository for:

Yaniv Benny, Niv Pekar, Lior Wolf. "Scale-Localized Abstract Reasoning". CVPR 2021.

architecture

Requirements

Data

Code

Optional:

To reproduce the results, run:

  1. First training
    $ CUDA_VISIBLE_DEVICES=0 python train.py --dataset <DATASET> --data_dir <PATH-TO-DATASETS> --wd <WD> --multihead
  2. When first training is done
    $ CUDA_VISIBLE_DEVICES=0 python train.py --dataset <DATASET> --data_dir <PATH-TO-DATASETS> --wd <WD> --recovery --multihead --multihead_mode eprob

To run test only, add --recovery --test to the command.

Pretrained models

Download the pretrained models for PGM and RAVEN-FAIR here.
Put the model inside a folder <EXP-DIR>/<EXP-NAME>/save and specify --exp_dir <EXP-DIR> --exp_name <EXP-NAME> --recovery --test

Citation

We thank you for showing interest in our work. If our work was beneficial for you, please consider citing us using:

@inproceedings{benny2021scale,
  title={Scale-localized abstract reasoning},
  author={Benny, Yaniv and Pekar, Niv and Wolf, Lior},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={12557--12565},
  year={2021}
}

If you have any question, please feel free to contact us.