https://github.com/snap-stanford/virel
ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy
https://github.com/snap-stanford/virel
graph graph-neural-networks machine-learning reasoning representation-learning unsupervised-learning
Last synced: 10 months ago
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ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy
- Host: GitHub
- URL: https://github.com/snap-stanford/virel
- Owner: snap-stanford
- Created: 2022-06-30T00:46:56.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2024-08-10T04:22:11.000Z (almost 2 years ago)
- Last Synced: 2025-07-03T01:34:51.969Z (12 months ago)
- Topics: graph, graph-neural-networks, machine-learning, reasoning, representation-learning, unsupervised-learning
- Language: Python
- Homepage: http://snap.stanford.edu/virel/
- Size: 729 KB
- Stars: 10
- Watchers: 41
- Forks: 5
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy
[Paper](https://arxiv.org/abs/2207.00590) | [Poster](https://github.com/snap-stanford/virel/blob/master/poster.pdf) | [Project Page](http://snap.stanford.edu/virel/)
This is the official repo for [ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy](https://arxiv.org/abs/2207.00590) (Zeng et al. ICML 2022 Beyond Bayes Workshop). ViRel is a method for unsupervised discovery and learning of visual relations with graph-level analogy.
Abstract:
We introduce Visual Relations with graph-level analogy (ViRel), a method for unsupervised discovery and learning of visual relations with graph-level analogy. In a grid-world based dataset that test visual relation reasoning, it achieves above 95% accuracy in unsupervised relation classification, discovers the relation graph structure for most tasks, and further generalizes to unseen tasks with more complicated relational structures.
Installation
==========================================================================
First clone the directory. Then run the following command to initialize the submodules:
```bash
git submodule init; git submodule update
```
(If you encounter a "no permission" error, you may need to [add a new SSH key to your GitHub account](https://docs.github.com/en/authentication/connecting-to-github-with-ssh/adding-a-new-ssh-key-to-your-github-account).)
This repository also has the following dependencies:
- `PyTorch ` >= 1.9.1
- `pytorch-geometric ` >= 2.0.1
- networkx >= 2.6.3
The included `environment.yml` file can be used to create a conda environment with all dependencies installed:
```bash
conda env create -f environment.yml
conda activate virel
```
# Dataset
The dataset files can be generated using the BabyARC engine in the `BabyARC` submodule.
A example generated dataset can be downloaded from [here](https://drive.google.com/drive/folders/1i-PvyqCycKpPwrtGmQPFkXv9rFGrzOs9?usp=sharing) for the 3 object, 1 distractor object setting.
# Running
To run the code, use the following command:
```bash
python relation_analogy.py
```
To specify the hyperparameters, pass in the path to the .yaml file config, such as:
```bash
python relation_analogy.py --yaml config/main1.yaml
```
More example yaml config files can be found in the `config` subdirectory.
The hyperparameters can also be passed in via command line, see `get_default_args` in `args.py` for all possible args to pass in. If an arg is not passed in, the default value will be used in `get_default_args`. Specifying an arg in the .yaml file will take precedence over passing in command line values.
# Citation
If you find our work and/or our code useful, please cite us via:
```bibtex
@inproceedings{zeng2022virel,
title={ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy},
author={Zeng, Daniel and Wu, Tailin and Leskovec, Jure},
year={2022},
eprint={2207.00590},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```