ikodoh/Flipped-VQA-Data

Large Language Models are Temporal and Causal Reasoners for Video Question Answering

This is the official implementation of Flipped-VQA (EMNLP 2023) (arxiv) (demo).

Dohwan Ko^1*, Ji Soo Lee^1*, Wooyoung Kang², Byungseok Roh², Hyunwoo J. Kim¹.

¹Department of Computer Science and Engineering, Korea University ²Kakao Brain

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 (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, 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.

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.

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}
}