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# Large Language Models are Temporal and Causal Reasoners for Video Question Answering
This is the official implementation of Flipped-VQA (EMNLP 2023) ([arxiv](https://arxiv.org/abs/2310.15747)) ([demo](https://ikodoh.github.io/flipped_vqa_demo.html)).
> Dohwan Ko<sup>1*</sup>, Ji Soo Lee<sup>1*</sup>, Wooyoung Kang<sup>2</sup>, Byungseok Roh<sup>2</sup>, Hyunwoo J. Kim<sup>1</sup>.
>
><sup>1</sup>Department of Computer Science and Engineering, Korea University <sup>2</sup>Kakao Brain
[](https://paperswithcode.com/sota/video-question-answering-on-next-qa?p=large-language-models-are-temporal-and-causal) [](https://paperswithcode.com/sota/video-question-answering-on-situated?p=large-language-models-are-temporal-and-causal) [](https://paperswithcode.com/sota/video-question-answering-on-dramaqa?p=large-language-models-are-temporal-and-causal) [](https://paperswithcode.com/sota/video-question-answering-on-vlep?p=large-language-models-are-temporal-and-causal) [](https://paperswithcode.com/sota/video-question-answering-on-tvqa?p=large-language-models-are-temporal-and-causal)
<div align="center">
<img src="asset/main.png" width="900px" />
</div>
## Setup
To install requirements, run:
```
git clone https://github.com/mlvlab/Flipped-VQA.git
cd Flipped-VQA
mkdir pretrained
conda create -n flipped-vqa python=3.8
conda activate flipped-vqa
sh setup.sh
```
## Dataset & LLaMA Preparation
* You can download our preprocessed datasets (NExT-QA, STAR, DramaQA, VLEP and TVQA) in [huggingface](https://huggingface.co/datasets/ikodoh/Flipped-VQA-Data) (We also provide the fine-tuned model on each dataset).
```
git lfs install
git clone https://huggingface.co/datasets/ikodoh/Flipped-VQA-Data
mv ./Flipped-VQA-Data/data ./
mv ./Flipped-VQA-Data/checkpoint ./
unzip ./data/tvqa/tvqa_subtitles.zip -d ./data/tvqa
rm -rf Flipped-VQA-Data ./data/tvqa/tvqa_subtitles.zip
```
* You can download original LLaMA at [here](https://github.com/facebookresearch/llama/tree/llama_v1), and put the checkpoint in ```./pretrained```.
```
./pretrained
└─ llama
|─ 7B
| |─ consolidated.00.pth
| └─ params.json
|─ 13B
| :
|─ 33B
| :
└─ tokenizer.model
```
## Training LLaMA-VQA (LLaMA + Flipped-VQA)
### NExT-QA
```
torchrun --rdzv_endpoint 127.0.0.1:1234 --nproc_per_node 4 train.py --model 7B \
--max_seq_len 128 --batch_size 8 --epochs 5 --warmup_epochs 2 --bias 3.5 --tau 100. --max_feats 10 --dataset nextqa \
--blr 9e-2 --weight_decay 0.14 --output_dir ./checkpoint/nextqa --accum_iter 2 --vaq --qav
```
### STAR
```
torchrun --rdzv_endpoint 127.0.0.1:1234 --nproc_per_node 4 train.py --model 7B \
--max_seq_len 128 --batch_size 8 --epochs 5 --warmup_epochs 2 --bias 3 --tau 100. --max_feats 10 --dataset star \
--blr 9e-2 --weight_decay 0.16 --output_dir ./checkpoint/star --accum_iter 1 --vaq --qav
```
### DramaQA
```
torchrun --rdzv_endpoint 127.0.0.1:1234 --nproc_per_node 4 train.py --model 7B \
--max_seq_len 384 --batch_size 2 --epochs 5 --warmup_epochs 2 --bias 3 --tau 100. --max_feats 10 --dataset dramaqa \
--blr 9e-2 --weight_decay 0.10 --output_dir ./checkpoint/dramaqa --accum_iter 8 --vaq --qav
```
### VLEP
```
torchrun --rdzv_endpoint 127.0.0.1:1234 --nproc_per_node 4 train.py --model 7B \
--max_seq_len 256 --batch_size 4 --epochs 5 --warmup_epochs 2 --bias 3 --tau 100. --max_feats 10 --dataset vlep \
--blr 6e-2 --weight_decay 0.20 --output_dir ./checkpoint/vlep --accum_iter 8 --sub --qav
```
### TVQA
```
torchrun --rdzv_endpoint 127.0.0.1:1234 --nproc_per_node 8 train.py --model 7B \
--max_seq_len 650 --batch_size 1 --epochs 5 --warmup_epochs 2 --bias 3 --tau 100. --max_feats 10 --dataset tvqa \
--blr 7e-2 --weight_decay 0.02 --output_dir ./checkpoint/tvqa --dataset tvqa --accum_iter 4 --sub --vaq --qav
```
The fine-tuned checkpoints on each dataset are [here](https://huggingface.co/datasets/ikodoh/Flipped-VQA-Data).
## Evaluation
From the training command, simply replace ```train.py``` with ```eval.py``` and add ```--resume ./your/checkpoint.pth```.
## Acknowledgements
This repo is built upon [LLaMA-Adapter](https://github.com/OpenGVLab/LLaMA-Adapter).
## Citations
```
@inproceedings{ko2023large,
title={Large Language Models are Temporal and Causal Reasoners for Video Question Answering},
author={Ko, Dohwan and Lee, Ji Soo and Kang, Wooyoung and Roh, Byungseok and Kim, Hyunwoo J},
booktitle={EMNLP},
year={2023}
}
```
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