101 lines
3.7 KiB
Markdown
101 lines
3.7 KiB
Markdown
<div align="center">
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<h1> OLViT: Multi-Modal State Tracking via Attention-Based Embeddings for Video-Grounded Dialog </h1>
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**[Adnen Abdessaied][4], [Manuel von Hochmeister][5], [Andreas Bulling][6]** <br> <br>
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**COLING 2024**, Turin, Italy <img src="misc/italy.png" width="3%" align="center"> <br>
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**[[Paper][7]]**
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----------------
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<img src="misc/teaser.png" width="40%" align="middle"><br><br>
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</div>
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# Citation
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If you find our code useful or use it in your own projects, please cite our paper:
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```
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@InProceedings{abdessaied24_coling,
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author = {Abdessaied, Adnen and Hochmeister, Manuel and Bulling, Andreas},
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title = {{OLViT: Multi-Modal State Tracking via Attention-Based Embeddings for Video-Grounded Dialog}},
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booktitle = {Proceedings of the International Conference on Computational Linguistics (COLING)},
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month = {May},
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year = {2024},
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}
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```
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# Table of Contents
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* [Setup and Dependencies](#Setup-and-Dependencies)
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* [Download Data](#Download-Data)
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* [Training](#Training)
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* [Testing](#Testing)
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* [Results](#Results)
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* [Acknowledgements](#Acknowledgements)
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# Setup and Dependencies
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We implemented our model using Python 3.7, PyTorch 1.11.0 (CUDA 11.3, CuDNN 8.3.2) and PyTorch Lightning. We recommend to setup a virtual environment using Anaconda. <br>
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1. Install [git lfs][1] on your system
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2. Clone our repository to download a checpint of our best model and our code
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```shell
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git lfs install
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git clone this_repo.git
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```
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3. Create a conda environment and install dependencies
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```shell
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conda create -n olvit python=3.7
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conda activate olvit
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conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
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pip install pytorch-lightning==1.6.3
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pip install transformers==4.19.2
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pip install torchtext==0.12.0
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pip install wandb nltk pandas
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```
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# Download Data
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1. [DVD][2] and [SIMMC 2.1][3] data are included in this repository and will be downloaded using git lfs
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2. Setup the data by executing
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```shell
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chmod u+x setup_data.sh
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./setup_data.sh
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```
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3. This will unpack all the data necessary in ```data/dvd/``` and ```data/simmc/```
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# Training
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We trained our model on 3 Nvidia Tesla V100-32GB GPUs. The default hyperparameters need to be adjusted if your setup differs from ours.
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## DVD
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1. Adjust the config file for DVD according to your hardware specifications in ```config/dvd.json```
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2. Execute
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```shell
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CUDA_VISIBLE_DEVICES=0,1,2 python train.py --cfg_path config/dvd.json
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```
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3. Checkpoints will be saved in ```checkpoints/dvd/```
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## SIMMC 2.1
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1. Adjust the config file for SIMMC 2.1 according to your hardware specifications in ```config/simmc.json```
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2. Execute
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```shell
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CUDA_VISIBLE_DEVICES=0,1,2 python train.py --cfg_path config/simmc.json
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```
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3. Checkpoints will be saved in ```checkpoints/simmc/```
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# Testing
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1. Execute
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```shell
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CUDA_VISIBLE_DEVICES=0 python test.py --ckpt_path <PATH_TO_TRAINED_MODEL> --cfg_path <PATH_TO_CONFIG_OF_TRAINED_MODEL>
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```
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# Results
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Training using the default config and a similar hardware setup as ours will result in the following performance
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## DVD
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<img src="misc/results_dvd.png" width="100%" align="middle"><br><br>
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## SIMMC 2.1
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<img src="misc/results_simmc.png" width="50%" align="middle"><br><br>
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# Acknowledgements
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Our work relied on the codebases of [DVD][2] and [SIMMC][3]. Thanks to the authors for sharing their code.
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[1]: https://git-lfs.com/
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[2]: https://github.com/facebookresearch/DVDialogues/
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[3]: https://github.com/facebookresearch/simmc2/
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[4]: https://perceptualui.org/people/abdessaied/
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[5]: https://www.linkedin.com/in/manuel-von-hochmeister-285416202/
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[6]: https://www.perceptualui.org/people/bulling/
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[7]: https://perceptualui.org/publications/abdessaied24_coling.pdf
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