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VD-GR: Boosting Visual Dialog with Cascaded Spatial-Temporal Multi-Modal GRaphs

Adnen Abdessaied,   Lei Shi,   Andreas Bulling

WACV'24, Hawaii, USA
[Paper]




Citation

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

@InProceedings{Abdessaied_2024_WACV,
    author    = {Abdessaied, Adnen and Shi, Lei and Bulling, Andreas},
    title     = {VD-GR: Boosting Visual Dialog With Cascaded Spatial-Temporal Multi-Modal Graphs},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2024},
    pages     = {5805-5814}
}

Table of Contents

Setup and Dependencies

We implemented our model using Python 3.7 and PyTorch 1.11.0 (CUDA 11.3, CuDNN 8.2.0). We recommend to setup a virtual environment using Anaconda.

  1. Install git lfs on your system
  2. Clone our repository to download the data, checkpoints, and code
    git lfs install
    git clone https://git.hcics.simtech.uni-stuttgart.de/public-projects/VDGR.git
    
  3. Create a conda environment and install dependencies
    conda create -n vdgr python=3.7
    conda activate vdgr
    conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
    conda install pyg -c pyg  # 2.1.0
    pip install pytorch-transformers
    pip install pytorch_pretrained_bert
    pip install pyhocon glog wandb lmdb
    
  4. If you wish to speed-up training, we recommend installing apex
    git clone https://github.com/NVIDIA/apex
    cd apex
    # if pip >= 23.1 (ref: https://pip.pypa.io/en/stable/news/#v23-1) which supports multiple `--config-settings` with the same key... 
    pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./
    # otherwise
    pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option="--cpp_ext" --global-option="--cuda_ext" ./
    cd .. 
    

Download Data

  1. Download the extacted visual features of VisDial and setup all files we used in our work. We provide a shell script for convenience:
./setup_data.sh  # Please make sure you have enough disk space

If everything was correctly setup, the data/ folder should look like this

├── history_adj_matrices
│   ├── test
│        ├── *.pkl
│   ├── train
│        ├── *.pkl
│   ├── val
│        ├── *.pkl
├── question_adj_matrices
│   ├── test
│        ├── *.pkl
│   ├── train
│        ├── *.pkl
│   ├── val
│        ├── *.pkl
├── img_adj_matrices
│   ├── *.pkl
├── parse_vocab.pkl
├── test_dense_mapping.json
├── tr_dense_mapping.json
├── val_dense_mapping.json
├── visdial_0.9_test.json
├── visdial_0.9_train.json
├── visdial_0.9_val.json
├── visdial_1.0_test.json
├── visdial_1.0_train_dense_annotations.json
├── visdial_1.0_train_dense.json
├── visdial_1.0_train.json
├── visdial_1.0_val_dense_annotations.json
├── visdial_1.0_val.json
├── visdialconv_dense_annotations.json
├── visdialconv.json
├── vispro_dense_annotations.json
└── vispro.json

Pre-trained Checkpoints

For convenience, we provide checkpoints of our model after the warm-up training stage in ckpt/ for both VisDial v1.0 and VisDial v0.9.
These checkpoints will be downloaded with the code if you use git lfs.

Training

We trained our model on 8 Nvidia Tesla V100-32GB GPUs. The default hyperparameters in config/vdgr.conf and config/bert_base_6layer_6conect.json need to be adjusted if your setup differs from ours.

Phase 1

Training

  1. In this phase, we train our model on VisDial v1.0 via
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py \
--model vdgr/P1 \
--mode train \
--tag K2_v1.0 \
--wandb_mode online \
--wandb_project your_wandb_project_name

⚠️ On a similar setup to ours, this will take roughly 20h to complete using apex for training.

  1. To train on VisDial v0.9:
    • Set visdial_version = 0.9 in config/vdgr.conf
    • Set start_path = ckpt/vdgr_visdial_v0.9_after_warmup_K2.ckpt in config/vdgr.conf
    • Run
     CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py \
     --model vdgr/P1 \
     --mode train \
     --tag K2_v0.9 \
     --wandb_mode online \
     --wandb_project your_wandb_project_name
    

Inference

  1. For inference on VisDial v1.0 val, VisDialConv, or VisPro:
    • Set eval_dataset = {visdial, visdial_conv, visdial_vispro} in logs/vdgr/P1_K2_v1.0/code/config/vdgr.conf
    • Run
     CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py \
     --model vdgr/P1 \
     --mode eval \
     --eval_dir logs/vdgr/P1_K2_v1.0 \
     --wandb_mode offline \
    
  2. For inference on VisDial v0.9:
    • Set eval_dataset = visdial in logs/vdgr/P1_K2_v0.9/code/config/vdgr.conf
    • Run
     CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py \
     --model vdgr/P1 \
     --mode eval \
     --eval_dir logs/vdgr/P1_K2_v0.9 \
     --wandb_mode offline \
    

⚠️ This might take some time to finish as the testing data of VisDial v0.9 is large.

  1. For inference on the visdial_v1.0 test:
    • Run
     CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py \
     --model vdgr/P1 \
     --mode predict \
     --eval_dir logs/vdgr/P1_K2_v1.0 \
     --wandb_mode offline \
    
    • The output file will be saved in output/

Phase 2

In this phase, we finetune on dense annotations to improve the NDCG score (Only supported for VisDial v1.0.)

  1. Run
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py \
--model vdgr/P2_CE \
--mode train \
--tag K2_v1.0_CE \
--wandb_mode online \
--wandb_project your_wandb_project_name

⚠️This will take roughly 3-4 hours to complete using the same setup as before and DP for training.

  1. For inference on VisDial v1.0:
    • Run:
    CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py \
    --model vdgr/P2_CE \
    --mode predict \
    --eval_dir logs/vdgr/P1_K2_v1.0_CE \
    --wandb_mode offline \
    
    • The output file will be saved in output/

Phase 3

Training

In the final phase, we train an ensemble method comprising of 8 models using K={1,2,3,4} and dense_loss={ce, listnet}. For K=k:

  1. Set the value of num_v_gnn_layers, num_q_gnn_layers, num_h_gnn_layers to k
  2. Set start_path = ckpt/vdgr_visdial_v1.0_after_warmup_K[k].ckpt in config/vdgr.conf (P1)
  3. Phase 1 training:
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py \
--model vdgr/P1 \
--mode train \
--tag K[k]_v1.0 \
--wandb_mode online \
--wandb_project your_wandb_project_name
  1. Set start_path = logs/vdgr/P1_K[k]_v1.0/epoch_best.ckpt in config/vdgr.conf (P2)
  2. Phase 2 training:
  • Fine-tune with CE:
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py \
--model vdgr/P2_CE \
--mode train \
--tag K[k]_v1.0_CE \
--wandb_mode online \
--wandb_project your_wandb_project_name
  • Fine-tune with LISTNET:
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py \
--model vdgr/P2_LISTNET \
--mode train \
--tag K[k]_v1.0_LISTNET \
--wandb_mode online \
--wandb_project your_wandb_project_name

Inference

  1. For inference on VisDial v1.0 test:
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py \
--model vdgr/P2_[CE,LISTNET] \
--mode predict \
--eval_dir logs/vdgr/P2_K[1,2,3,4]_v1.0_[CE,LISTNET] \
--wandb_mode offline \
  1. Finally, merge the outputs of all models
 python ensemble.py \
--exp test \
--mode predict \

The output file will be saved in output/

Results

VisDial v0.9

Model MRR R@1 R@5 R@10 Mean
Prev. SOTA 71.99 59.41 87.92 94.59 2.87
VD-GR 74.50 62.10 90.49 96.37 2.45

VisDialConv

Model NDCG MRR R@1 R@5 R@10 Mean
Prev. SOTA 61.72 61.79 48.95 77.50 86.71 4.72
VD-GR 67.09 66.82 54.47 81.71 91.44 3.54

VisPro

Model NDCG MRR R@1 R@5 R@10 Mean
Prev. SOTA 59.30 62.29 48.35 80.10 88.87 4.37
VD-GR 60.35 69.89 57.21 85.97 92.68 3.15

VisDial V1.0 Val

Model NDCG MRR R@1 R@5 R@10 Mean
Prev. SOTA 65.47 69.71 56.79 85.82 93.64 3.15
VD-GR 64.32 69.91 57.01 86.14 93.74 3.13

VisDial V1.0 Test

Model NDCG MRR R@1 R@5 R@10 Mean
Prev. SOTA 64.91 68.73 55.73 85.38 93.53 3.21
VD-GR 63.49 68.65 55.33 85.58 93.85 3.20
♣️ Prev. SOTA 75.92 56.18 45.32 68.05 80.98 5.42
♣️ VD-GR 75.95 58.30 46.55 71.45 84.52 5.32
♣️♦️ Prev. SOTA 76.17 56.42 44.75 70.23 84.52 5.47
♣️♦️ VD-GR 76.43 56.35 45.18 68.13 82.18 5.79

♣️ = Finetuning on dense annotations, ♦️ = Ensemble model

Contributors

For any questions or enquiries, don't hesitate to contact the above contributor(s).