Awesome

eXplainable and eXplicit Neural Modules (XNMs)

Pytorch implementation of paper

Explainable and Explicit Visual Reasoning over Scene Graphs <br> Jiaxin Shi, Hanwang Zhang, Juanzi Li

Flowchart of our model:

A visualization of our reasoning process:

If you find this code useful in your research, please cite

@inproceedings{shi2019explainable,
  title={Explainable and Explicit Visual Reasoning over Scene Graphs},
  author={Jiaxin Shi, Hanwang Zhang, Juanzi Li},
  booktitle={CVPR},
  year={2019}
}

Requirements

• python==3.6
• pytorch==0.4.0
• h5py
• tqdm
• matplotlib

Experiments

We have 4 experiment settings:

• CLEVR dataset, Det setting (i.e., using detected scene graphs). Codes are in the directory ./exp_clevr_detected.
• CLEVR dataset, GT setting (i.e., using ground truth scene graphs), attention is computed by softmax function over the label space. Codes are in ./exp_clevr_gt_softmax.
• CLEVR dataset, GT setting, attention is computed by sigmoid function. Codes are in ./exp_clevr_gt_sigmoid.
• VQA2.0 dataset, detected scene graphs. Codes are in ./exp_vqa.

We have a separate README for each experiment setting as an instruction to reimplement our reported results. Feel free to contact me if you have any problems: shijx12@gmail.com

Acknowledgement

• We refer to the repo clevr-iep for preprocessing codes.
• Our implementations of module model and dataloader are based on tbd-net.
• Our stacked neural module implementation in ./exp_vqa is based on Hu's StackNMN.