ReasoningConsistency-VQA

• C. Jing, Y. Jia, Y. Wu, X. Liu, Q. Wu, Maintaining Reasoning Consistency in Compositional Visual Question Answering. in CVPR 2022 (PDF)

GQA-Sub Dataset

We build a GQA-Sub dataset to enable the quantitative evaluation of reasoning consistency in compositional VQA. The GQA-Sub dataset is constructed based on the GQA dataset, a large-scale dataset for real-world visual reasoning and compositional question answering. We only generate sub-questions for questions of the train split and the validation split of GQA because the ground-truth scene graphs of the two splits are available. Thus our GQA-Sub dataset contains a train-sub split and a validation-sub split. The two splits can be found in the folder "questions".

Dialog-like Reasoning Method

We propose a dialog-like reasoning method that integrates the reasoning processes for sub-questions into the reasoning process for a compositional question to maintain the reasoning consistency in compositional VQA. The folder "DLR" contains the source code of the proposed method.

Prerequisites

• NVIDIA Driver & CUDA & cuDNN
• Python 3.6
• Pytorch 1.9.1.post3
• numpy 1.19.5
• h5py 3.1.0
• PyYAML 6.1
• file_utils 0.0.1

Train and evaluate

Step 1: download data

Please download all the question files from here and the visual features from here.

Step 2: training

cd dir/
python exp/main.py exp_id 001 dialog True TRAIN.SPLIT_VQA train_dialog_balanced

Step 3: evaluation

python exp/main.py train False TEST.EVAL_ID 001 TEST.EPOCH 10 TEST.DUMP_PRED True TEST.SPLIT_VQA val_sub_balanced
python exp/main.py train False TEST.EVAL_ID 001 TEST.EPOCH 10 TEST.DUMP_PRED True TEST.SPLIT_VQA val_balanced 
python util/compute_consistency.py --exp_name 001_DLR --epoch 10

Citation

If you find the dataset or code useful, please consider citing our paper:

@inproceedings{jing2022maintaining,
  title={Maintaining Reasoning Consistency in Compositional Visual Question Answering},
  author={Jing, Chenchen and Jia, Yunde and Wu, Yuwei and Liu, Xinyu and Wu, Qi},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={5099-5108},
  year={2022}
}

Acknowledgment

The implementation of the dialog-like reasoning method is partly based on the following codebases, LCGN, MMN, and Logic-guided QA. We gratefully thank the authors for their wonderful works.