Neural Systematic Binder

ICLR 2023

[arXiv] [project] [datasets] [openreview]

This is the official PyTorch implementation of Neural Systematic Binder.

Authors

Gautam Singh and Yeongbin Kim and Sungjin Ahn

Datasets

The datasets tested in the paper (CLEVR-Easy, CLEVR-Hard, and CLEVR-Tex) can be downloaded via this link.

Training

To train the model, simply execute:

python train.py

Check train.py to see the full list of training arguments. You can use the --data_path argument to point to the set of images via a glob pattern.

Outputs

The training code produces Tensorboard logs. To see these logs, run Tensorboard on the logging directory that was provided in the training argument --log_path. These logs contain the training loss curves and visualizations of reconstructions and object attention maps.

Packages Required

The following packages may need to be installed first.

• PyTorch
• TensorBoard for logging.

Evaluation

The evaluation scripts are provided in branch evaluate.

Citation

@inproceedings{
      singh2023sysbinder,
      title={Neural Systematic Binder},
      author={Gautam Singh and Yeongbin Kim and Sungjin Ahn},
      booktitle={International Conference on Learning Representations},
      year={2023},
      url={https://openreview.net/forum?id=ZPHE4fht19t}
}