Deep Multimodal Fusion by Channel Exchanging

By Yikai Wang, Wenbing Huang, Fuchun Sun, Tingyang Xu, Yu Rong, Junzhou Huang.

[Paper] [Paper & Appendix] (with proofs and visualizations)

This repository is an official PyTorch implementation of "Deep Multimodal Fusion by Channel Exchanging", in NeurIPS 2020. The basic method and applications are introduced as belows:

If you find our work useful for your research, please consider citing the following paper.

@inproceedings{wang2020cen,
  title={Deep Multimodal Fusion by Channel Exchanging},
  author={Wang, Yikai and Huang, Wenbing and Sun, Fuchun and Xu, Tingyang and Rong, Yu and Huang, Junzhou},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2020}
}

Datasets

For semantic segmentation task on NYUDv2 (official dataset), we provide a link to download the dataset here. The provided dataset is originally preprocessed in this repository, and we add depth data in it.

For image-to-image translation task, we use the sample dataset of Taskonomy, where a link to download the sample dataset is here.

Please modify the data paths in the codes, where we add comments 'Modify data path'.

Dependencies

python==3.6.2
pytorch==1.0.0
torchvision==0.2.2
imageio==2.4.1
numpy==1.16.2
scikit-learn==0.20.2
scipy==1.1.0
opencv-python==4.0.0

Semantic Segmentation

First,

cd semantic_segmentation

Training script for segmentation with RGB and Depth input, the default setting uses RefineNet (ResNet101),

python main.py --gpu 0 -c exp_name  # or --gpu 0 1 2

Or, for training RefineNet with ResNet152,

python main.py --gpu 0 --enc 152 --num-epoch 150 150 150 -c exp_name  # or --gpu 0 1 2

Evaluation script,

python main.py --gpu 0 --resume path_to_pth --evaluate  # optionally use --save-img to visualize results

Checkpoint models, training logs and the single-scale performance on NYUDv2 (with RefineNet) are provided as follows:

Backbone	Pixel Acc. (%)	Mean Acc. (%)	Mean IoU (%)	Download
ResNet101	76.2	62.8	51.1	Google Drive
ResNet152	77.0	64.4	51.6	Google Drive

Image-to-Image Translation

First,

cd image2image_translation

Training script, an example of translation from Shade (2) and Texture (7) to RGB (0) (could reach 62~63 FID score),

python main.py --gpu 0 --img-types 2 7 0 -c exp_name

This script will auto-evaluate on the validation dataset every 5 training epochs.

Predicted images will be automatically saved during training, in the following folder structure:

code_root/ckpt/exp_name/results
  ├── input0  # 1st modality input
  ├── input1  # 2nd modality input
  ├── fake0   # 1st branch output 
  ├── fake1   # 2nd branch output
  ├── fake2   # ensemble output
  ├── best    # current best output
  │    ├── fake0
  │    ├── fake1
  │    └── fake2
  └── real    # ground truth output

For training with other modalities, the index for each img-type is described as belows, and also in Line 69 of main.py.

0: 'rgb', 1: 'normal', 2: 'reshading', 3: 'depth_euclidean', 
4: 'depth_zbuffer', 5: 'principal_curvature', 6: 'edge_occlusion', 
7: 'edge_texture', 8: 'segment_unsup2d', 9: 'segment_unsup25d'

Full quantitative results are provided in the paper.

License

CEN is released under MIT License.

miao19980215 / CEN