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| # OpenVQA | |
| <div> | |
| <a href="https://openvqa.readthedocs.io/en/latest/?badge=latest"><img alt="Documentation Status" src="https://readthedocs.org/projects/openvqa/badge/?version=latest"/></a> | |
| <a href="https://github.com/MILVLG"><img alt="powered-by MILVLG" src="https://img.shields.io/badge/powered%20by-MILVLG-orange.svg?style=flat&colorA=E1523D&colorB=007D8A"/></a> | |
| </div> | |
| OpenVQA is a general platform for visual question ansering (VQA) research, with implementing state-of-the-art approaches (e.g., [BUTD](https://arxiv.org/abs/1707.07998), [MFH](https://arxiv.org/abs/1708.03619), [BAN](https://arxiv.org/abs/1805.07932), [MCAN](https://arxiv.org/abs/1906.10770) and [MMNasNet](https://arxiv.org/pdf/2004.12070.pdf)) on different benchmark datasets like [VQA-v2](https://visualqa.org/), [GQA](https://cs.stanford.edu/people/dorarad/gqa/index.html) and [CLEVR](https://cs.stanford.edu/people/jcjohns/clevr/). Supports for more methods and datasets will be updated continuously. | |
| <p align="center"> | |
| <img src="misc/openvqa_overall.png" width="550"> | |
| </p> | |
| ## Documentation | |
| Getting started and learn more about OpenVQA [here](https://openvqa.readthedocs.io/en/latest/). | |
| ## Benchmark and Model Zoo | |
| Supported methods and benchmark datasets are shown in the below table. | |
| Results and models are available in [MODEL ZOO](https://openvqa.readthedocs.io/en/latest/basic/model_zoo.html). | |
| | | [VQA-v2](https://visualqa.org/) | [GQA](https://cs.stanford.edu/people/dorarad/gqa/index.html) | [CLEVR](https://cs.stanford.edu/people/jcjohns/clevr/) | | |
| |:-----------------------------------------:|:-------------------------------:|:------------------------------------------------------------:|:------------------------------------------------------:| | |
| | [BUTD](https://arxiv.org/abs/1707.07998) | ✓ | ✓ | | | |
| | [MFB](https://arxiv.org/abs/1708.01471v1) | ✓ | | | | |
| | [MFH](https://arxiv.org/abs/1708.03619) | ✓ | | | | |
| | [BAN](https://arxiv.org/abs/1805.07932) | ✓ | ✓ | | | |
| | [MCAN](https://arxiv.org/abs/1906.10770) | ✓ | ✓ | ✓ | | |
| | [MMNasNet](https://arxiv.org/pdf/2004.12070.pdf) | ✓ | | | | |
| ## News & Updates | |
| #### v0.7.5 (30/12/2019) | |
| - Add supports and pre-trained models for the approaches on CLEVR. | |
| #### v0.7 (29/11/2019) | |
| - Add supports and pre-trained models for the approaches on GQA. | |
| - Add an document to tell developers how to add a new model to OpenVQA. | |
| #### v0.6 (18/09/2019) | |
| - Refactoring the documents and using Sphinx to build the whole documents. | |
| #### v0.5 (31/07/2019) | |
| - Implement the basic framework for OpenVQA. | |
| - Add supports and pre-trained models for BUTD, MFB, MFH, BAN, MCAN on VQA-v2. | |
| ## License | |
| This project is released under the [Apache 2.0 license](LICENSE). | |
| ## Contact | |
| This repo is currently maintained by Zhou Yu ([@yuzcccc](https://github.com/yuzcccc)) and Yuhao Cui ([@cuiyuhao1996](https://github.com/cuiyuhao1996)). | |
| ## Citation | |
| If this repository is helpful for your research or you want to refer the provided results in the modelzoo, you could cite the work using the following BibTeX entry: | |
| ``` | |
| @misc{yu2019openvqa, | |
| author = {Yu, Zhou and Cui, Yuhao and Shao, Zhenwei and Gao, Pengbing and Yu, Jun}, | |
| title = {OpenVQA}, | |
| howpublished = {\url{https://github.com/MILVLG/openvqa}}, | |
| year = {2019} | |
| } | |