SMMCL

PyTorch implementation of "Understanding Dark Scenes by Contrasting Multi-Modal Observations"
[WACV paper, supp]

Updates

[2023/10/24] Our paper was accepted to WACV 2024. See you in Hawaii.
[2024/04/22] The code for low-light indoor scenes (the LLRGBD dataset) has been uploaded.
[2024/08/02] The supplementary material has been updated. Previously there were errors in Figures III, V, and VI.

Preparation

• Python 3.10.6, Torch 1.12.1, CUDA 10.2, requirements.txt
• Download the datasets and put them in /datasets.

Train

Download the pretrained weight of SegNext-B and put it in /pretrained/segnext.

cd /path/to/SMMCL_LLRGBD or /path/to/SMMCL_MFNet or /path/to/SMMCL_NYU
# modify config.py
python train.py -d 0-3 

Test

Quick Start: Download our pretrained weights and put them in /SMMCL_XXX/log_XXX_mscan_b/checkpoint.

cd /path/to/SMMCL_LLRGBD
python eval.py -d 0-3 -e 500 

cd /path/to/SMMCL_MFNet
python eval.py -d 0-3 -e 300 

cd /path/to/SMMCL_NYU
python eval.py -d 0-3 -e 600

Acknowledgement

Our code was built based on the repositories of CMX and MSCSCL. We thank the authors for their efforts.

Citation

@InProceedings{SMMCL_2024_WACV,
  author    = {Dong, Xiaoyu and Yokoya, Naoto},
  title     = {Understanding Dark Scenes by Contrasting Multi-Modal Observations},
  booktitle = {WACV},
  year      = {2024}
}