MST-MIXER/README.md

MST-MIXER : Multi-Modal Video Dialog State Tracking in the Wild

Adnen Abdessaied,   Lei Shi,   Andreas Bulling

ECCV 2024, Milan, Italy
[Paper]

---

Citation

If you find our code useful or use it in your own projects, please cite our paper:

@InProceedings{Abdessaied_2024_eccv,
    author    = {Abdessaied, Adnen and Shi, Lei and Bulling, Andreas},
    title     = {{Multi-Modal Video Dialog State Tracking in the Wild}},
    booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
    year      = {2024}
    }

Table of Contents

• Setup and Dependencies
• Download Data
• Training
• Response Generation
• Results
• Acknowledgements

Setup and Dependencies

We implemented our model using Python 3.7 and PyTorch 1.12.0 (CUDA 11.3, CuDNN 8.3.2). We recommend to setup a virtual environment using Anaconda.

1. Install git lfs on your system
2. Clone our repository to download a checpint of our best model and our code

       git lfs install
       git clone this_repo.git
   

3. Create a conda environment and install dependencies

       conda create -n mst_mixer python=3.7
       conda activate mst_mixer
       conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch
       conda install pyg -c pyg
       conda install pytorch-scatter -c pyg  # pytorch >= 1.8.0
       conda install pytorch-sparse -c pyg  # pytorch >= 1.8.0
       conda install -c huggingface transformers
       pip install evaluate wandb glog pyhocon attrs
   

Download Data

AVSD

1. Download the AVSD-DSTC7, AVSD-DSTC8 and AVSD-DSTC10 data
2. Place the raw json files in raw_data/ and the features in features/
3. Prepeocess and save the input features for faster training as indicated in custom_datasets/

NExT-QA

1. For convenience, we included the features/data in this git repo.

Training

We trained our model on 8 Nvidia Tesla V100-32GB GPUs. The default hyperparameters in config/mst_mixer.conf need to be adjusted if your setup differs from ours.

AVSD

1. Set task=avsd in config/mst_mixer.conf

2. CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py \
   --mode train \ 
   --tag mst_mixer_avsd \
   --wandb_mode online \
   --wandb_project mst_mixer_avsd
   

To deactivate wandb logging, use --wandb_mode disabled. On a similar setup to ours, this will take roughly 20h to complete.

NExT-QA

1. Set task=nextqa in config/mst_mixer.conf

2. CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py \
   --mode train \ 
   --tag mst_mixer_nextqa \
   --wandb_mode online \
   --wandb_project mst_mixer_nextqa
   

Response Generation

AVSD-DSTC7

1. Set dstc=7 in the .conf file of your trained networks. in The default setting, can find this under logs/unique_training_tag/code/config/mst_mixer.conf
2. Generate the responses

./generate_parallel_avsd.sh mst_mixer/mixer results_avsd_dstc7 generate logs/mst_mixer_avsd 7

3. All responses will be saved in output/dstc7/

AVSD-DSTC8

1. Set dstc=8 in the .conf file of your trained networks. in The default setting, can find this under logs/unique_training_tag/code/config/mst_mixer.conf
2. Generate the responses

./generate_parallel_avsd.sh mst_mixer/mixer results_avsd_dstc8 generate logs/mst_mixer_avsd 8

3. All responses will be saved in output/dstc8/

AVSD-DSTC10

1. Set dstc=10 in the .conf file of your trained networks. in The default setting, can find this under logs/unique_training_tag/code/config/mst_mixer.conf
2. Generate the responses

./generate_parallel_avsd.sh mst_mixer/mixer results_avsd_dstc10 generate logs/mst_mixer_avsd 10

3. All responses will be saved in output/dstc10/

NExT-QA

1. Generate the responses

./generate_parallel_nextqa.sh mst_mixer/mixer results_nextqa generate logs/mst_mixer_nextqa

2. All responses will be saved in output/nextqa/
3. Evalute using this script

Results

To evaluate our best model on

AVSD-DSTC7

Executing the eval_tool of AVSD-DSTC7 using the generated repsonses will output the following metrics

Model	BLUE-1	BLUE-2	BLUE-3	BLUE-4	METEOR	ROUGE-L	CIDEr
Prev. SOTA	78.2	65.5	55.2	46.9	30.8	61.9	135.2
MST_MIXER	78.7	66.5	56.3	47.6	31.3	62.5	138.8

AVSD-DSTC8

1. Set dstc=8 in the ckpt/code/mst_mixer.conf
2. run

./generate_parallel_avsd.sh mst_mixer/mixer results_avsd_dstc8_best_model generate ckpt/avsd 8

3. The responses will be saved in output/dstc8/
4. Executing the eval_tool of AVSD-DSTC8 using the generated repsonses will output the following metrics

Model	BLUE-1	BLUE-2	BLUE-3	BLUE-4	METEOR	ROUGE-L	CIDEr
Prev. SOTA	76.4	64.1	54.3	46.0	30.1	61.0	130.4
MST_MIXER	77.5	66.0	56.1	47.7	30.6	62.4	135.4

AVSD-DSTC10

Executing the eval_tool of AVSD-DSTC10 using the generated repsonses will output the following metrics

Model	BLUE-1	BLUE-2	BLUE-3	BLUE-4	METEOR	ROUGE-L	CIDEr
Prev. SOTA	69.3	55.6	45.0	37.2	24.9	53.6	91.2
MST_MIXER	70.0	57.4	47.6	40.0	25.7	54.5	99.8

NExT-QA

Executing the eval script of NExT-QA using the generated repsonses will output the following metrics

Model	WUPS_C	WUPS_T	WUPS_D	WUPS
Prev. SOTA	17.98	17.95	50.84	28.40
MST_MIXER	22.12	22.20	55.64	29.50

Acknowledgements

We thank the authors of RLM for providing their code that greatly influenced this work.