This repository contains implementation of our AAAI 2021 paper.
Stratified Rule-Aware Network for Abstract Visual Reasoning
Sheng Hu*, Yuqing Ma*, Xianglong Liu†, Yanlu Wei, Shihao Bai
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2021
(* equal contribution, † corresponding author)
To fix the defacts of RAVEN dataset, we generate an alternative answer set for each RPM question in RAVEN, forming an improved dataset named Impartial-RAVEN (I-RAVEN for short). The comparison between the two datasets is shown below. For more details, please refer to our paper.
Code to generate the dataset resides in the I-RAVEN folder. The dependencies are consistent with the original RAVEN.
- Python 2.7
- OpenCV
- numpy
- tqdm
- scipy
- pillow
See I-RAVEN/requirements.txt for a full list of packages required. To install the dependencies, run
pip install -r I-RAVEN/requirements.txt
To generate a dataset, run
python I-RAVEN/main.py --num-samples <number of samples per configuration> --save-dir <directory to save the dataset>
Check the I-RAVEN/main.py file for a full list of arguments you can adjust.
Code of our model resides in the SRAN folder. The requirements are listed as follows:
- Python 3.7
- CUDA
- PyTorch
- torchvision
- scipy 1.1.0
- Visdom
See SRAN/requirements.txt for a full list of packages required. To install the dependencies, run
pip install -r SRAN/requirements.txt
To view training results, run python -m visdom.server -p 9527 and click the URL http://localhost:9527.
To train and evaluate the model, run
python SRAN/main.py --dataset <I-RAVEN or PGM> --dataset_path <path to the dataset> --save <directory to save the checkpoint>
Check the SRAN/main.py file for a full list of arguments you can adjust.
Performance on I-RAVEN:
| Model | Acc | Center | 2x2G | 3x3G | O-IC | O-IG | L-R | U-D |
|---|---|---|---|---|---|---|---|---|
| LSTM | 18.9% | 26.2% | 16.7% | 15.1% | 21.9% | 21.1% | 14.6% | 16.5% |
| WReN [code] | 23.8% | 29.4% | 26.8% | 23.5% | 22.5% | 21.5% | 21.9% | 21.4% |
| ResNet | 40.3% | 44.7% | 29.3% | 27.9% | 46.2% | 35.8% | 51.2% | 47.4% |
| ResNet+DRT [code] | 40.4% | 46.5% | 28.8% | 27.3% | 46.0% | 34.2% | 50.1% | 49.8% |
| LEN [code] | 41.4% | 56.4% | 31.7% | 29.7% | 52.1% | 31.7% | 44.2% | 44.2% |
| Wild ResNet | 44.3% | 50.9% | 33.1% | 30.8% | 50.9% | 38.7% | 53.1% | 52.6% |
| CoPINet [code] | 46.1% | 54.4% | 36.8% | 31.9% | 52.2% | 42.8% | 51.9% | 52.5% |
| SRAN (Ours) | 60.8% | 78.2% | 50.1% | 42.4% | 68.2% | 46.3% | 70.1% | 70.3% |
Performance on PGM:
| Model | LSTM | ResNet | Wild ResNet | CoPINet | WReN | MXGNet | LEN | SRAN (Ours) |
|---|---|---|---|---|---|---|---|---|
| Acc | 35.8% | 42.0% | 48.0% | 56.4% | 62.6% | 66.7% | 68.1% | 71.3% |
If you find our work helpful, please cite us.
@inproceedings{hu2021stratified,
title={Stratified Rule-Aware Network for Abstract Visual Reasoning},
author={Hu, Sheng and Ma, Yuqing and Liu, Xianglong and Wei, Yanlu and Bai, Shihao},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
volume={35},
number={2},
pages={1567--1574},
year={2021}
}