CMCIR

Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering
IEEE Transactions on Pattern Analysis and Machine Intelligence 2023
For more details, please refer to our paper Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering
中文解读

Abstract

Existing visual question answering methods often suffer from cross-modal spurious correlations and oversimplified event-level reasoning processes that fail to capture event temporality, causality, and dynamics spanning over the video. In this work, to address the task of event-level visual question answering, we propose a framework for cross-modal causal relational reasoning. In particular, a set of causal intervention operations is introduced to discover the underlying causal structures across visual and linguistic modalities. Our framework, named Cross-Modal Causal RelatIonal Reasoning (CMCIR), involves three modules: i) Causality-aware Visual-Linguistic Reasoning (CVLR) module for collaboratively disentangling the visual and linguistic spurious correlations via front-door and back-door causal interventions; ii) Spatial-Temporal Transformer (STT) module for capturing the fine-grained interactions between visual and linguistic semantics; iii) Visual-Linguistic Feature Fusion (VLFF) module for learning the global semantic-aware visual-linguistic representations adaptively. Extensive experiments on four event-level datasets demonstrate the superiority of our CMCIR in discovering visual-linguistic causal structures and achieving robust event-level visual question answering.

Model

Figure 1: Framework of our proposed CMCIR.

Experimental Results

Figure 2: Results on SUTD-TrafficQA dataset.
Figure 3: Results on TGIF-QA dataset.
Figure 4: Results on MSVD-QA dataset.
Figure 5: Results on MSRVTT-QA dataset.

Requirements

• python3.7
• numpy
• pytorch
• pytorch-geometric

Datasets

We conducted our experiment on large-scale event-level urban dataset SUTD-TrafficQA and three benchmark real-world datasets TGIF-QA, MSVD-QA and MSRVTT-QA. The preprocessing steps are the same as the official ones. Please find more details from these datasets.

Setups

1. Download SUTD-TrafficQA, TGIF-QA, MSVD-QA and MSRVTT-QA datasets.
2. Edit absolute paths in preprocess/preprocess_features.py and preprocess/preprocess_questions.py upon where you locate your data.
3. Install dependencies.

Experiments with SUTD-TrafficQA

We refer to SUTD-TrafficQA Official Codes for preprocessing.

Preprocess Linguistic Features

1. Download glove pretrained 300d word vectors to /data/glove/ and process it into a pickle file.

python txt2pickle.py

2. Preprocess train/val/test questions:

python 1_preprocess_questions_oie.py --mode train
    
python 1_preprocess_questions_oie.py --mode test

Preprocess Visual Features

1. To extract appearance feature with Swin or Resnet101 model:
   Download Swin pretrained model (swin_large_patch4_window7_224_22k.pth) and place it to configs/.

python 1_preprocess_features_appearance.py --model Swin --question_type none

 or
 
python 1_preprocess_features_appearance.py --model resnet101 --question_type none

2. To extract motion feature with Swin or ResnetXt101 model:

Download Swin3D pretrained model (swin_base_patch244_window877_kinetics600_22k.pth) and place it to configs/.

Download ResNeXt-101 pretrained model (resnext-101-kinetics.pth) and place it to data/preprocess/pretrained/.

python 1_preprocess_features_motion.py --model Swin --question_type none

or

python 1_preprocess_features_motion.py --model resnext101 --question_type none

Visual K-means Clustering

1. To extract training appearance feature with Swin or Resnet101 model:

python 1_preprocess_features_appearance_train.py --model Swin --question_type none

 or
 
python 1_preprocess_features_appearance_train.py --model resnet101 --question_type none

2. To extract training motion feature with Swin or ResnetXt101 model:

python 1_preprocess_features_motion_train.py --model Swin --question_type none

or

python 1_preprocess_features_motion_train.py --model resnext101 --question_type none

3. K-means Clustering

python k_means.py

Edit absolute paths upon where you locate your data.

Training and Testing

python train_SUTD.py

Experiments with TGIF-QA

Depending on the task to chose question_type out of 4 options: action, transition, count, frameqa.

Preprocess Linguistic Features

1. Preprocess train/val/test questions:

python 1_preprocess_questions_oie_tgif.py --mode train --question_type {question_type}
    
python 1_preprocess_questions_oie_tgif.py --mode test  --question_type {question_type}

Preprocess Visual Features

1. To extract appearance feature with Swin or Resnet101 model:

python 1_preprocess_features_appearance_tgif_total.py --model Swin --question_type {question_type}

 or
 
python 1_preprocess_features_appearance_tgif_total.py --model resnet101 --question_type {question_type}

2. To extract motion feature with Swin or ResnetXt101 model:

python 1_preprocess_features_motion_tgif_total.py --model Swin --question_type {question_type}

or

python 1_preprocess_features_motion_tgif_total.py --model resnext101 --question_type {question_type}

Visual K-means Clustering

1. To extract training appearance feature with Swin or Resnet101 model:

python 1_preprocess_features_appearance_tgif.py --model Swin --question_type {question_type}

 or
 
python 1_preprocess_features_appearance_tgif.py --model resnet101 --question_type {question_type}

2. To extract training motion feature with Swin or ResnetXt101 model:

python 1_preprocess_features_motion_tgif.py --model Swin --question_type {question_type}

or

python 1_preprocess_features_motion_tgif.py --model resnext101 --question_type {question_type}

3. K-means Clustering

python k_means.py

Edit absolute paths upon where you locate your data.

Training and Testing

python train_TGIF_Action.py

python train_TGIF_Transition.py

python train_TGIF_Count.py

python train_TGIF_FrameQA.py

Experiments with MSVD-QA/MSRVTT-QA

Preprocess linguistic features

1. Preprocess train/val/test questions:

python 1_preprocess_questions_oie_msvd.py --mode train
    
python 1_preprocess_questions_oie_msvd.py --mode test

or

python 1_preprocess_questions_oie_msrvtt.py --mode train
    
python 1_preprocess_questions_oie_msrvtt.py --mode test

Preprocess visual features

1. To extract appearance feature with Swin or Resnet101 model:

python 1_preprocess_features_appearance_msvd.py --model Swin --question_type none

python 1_preprocess_features_appearance_msrvtt.py --model Swin --question_type none

 or
 
python 1_preprocess_features_appearance_msvd.py --model resnet101 --question_type none

python 1_preprocess_features_appearance_msrvtt.py --model resnet101 --question_type none

2. To extract motion feature with Swin or ResnetXt101 model:

python 1_preprocess_features_motion_msvd.py --model Swin --question_type none

python 1_preprocess_features_motion_msrvtt.py --model Swin --question_type none

or

python 1_preprocess_features_motion_msvd.py --model resnext101 --question_type none

python 1_preprocess_features_motion_msrvtt.py --model resnext101 --question_type none

Visual K-means Clustering

1. To extract training appearance feature with Swin or Resnet101 model:

python 1_preprocess_features_appearance_msvd_train.py --model Swin --question_type none

python 1_preprocess_features_appearance_msrvtt_train.py --model Swin --question_type none

 or
 
python 1_preprocess_features_appearance_msvd_train.py --model resnet101 --question_type none

python 1_preprocess_features_appearance_msrvtt_train.py --model resnet101 --question_type none

2. To extract training motion feature with Swin or ResnetXt101 model:

python 1_preprocess_features_motion_msvd_train.py --model Swin --question_type none

python 1_preprocess_features_motion_msrvtt_train.py --model Swin --question_type none

or

python 1_preprocess_features_motion_msvd_train.py --model resnext101 --question_type none

python 1_preprocess_features_motion_msrvtt_train.py --model resnext101 --question_type none

3. K-means Clustering

python k_means.py

Edit absolute paths upon where you locate your data.

Training and Testing

python train_MSVD.py

python train_MSRVTT.py

Citation

If you use this code for your research, please cite our paper.

@article{CMCIR,
  title={Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering},
  author={Liu, Yang and Li, Guanbin and Lin, Liang},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2023}
  doi={10.1109/TPAMI.2023.3284038}
}
@article{liu2022cross,
  title={Cross-modal causal relational reasoning for event-level visual question answering},
  author={Liu, Yang and Li, Guanbin and Lin, Liang},
  journal={arXiv preprint arXiv:2207.12647},
  year={2022}
}

If you have any question about this code, feel free to reach (liuy856@mail.sysu.edu.cn).