jingjunyi/cmformer

[AAAI2024] Learning Content-enhanced Mask Transformer for Domain Generalized Urban-scene Segmentation

CMFormer: Learning Content-enhanced Mask Transformer for Domain Generalized Urban-scene Segmentation

This is the official implementation of our work entitled as Learning Content-enhanced Mask Transformer for Domain Generalized Urban-scene Segmentation, which has been accepted by AAAI2024.

Methodology Overview

Recent work has shown that mask-level segmentation Transformer (e.g., Mask2Former) is a scalable learner for domain generalized semantic segmentation. Unfortunately, we empirically observed that, a mask-level representation is better at representing content but more sensitive to style variations; its low-resolution counterpart on the contrary is less capable to represent content but more robust to the style variations.

Overall, the mask representation and its down-sampled counterpart shows complementary properties when handling samples from different domains. Thus, it is natural to jointly leverage both mask representation and its down-sampled counterparts, so as to at the same time stabilizing the content and be insensitive to the style variation.

Environment Configuration

The development of CMFormer is largely based on Mask2Former [https://bowenc0221.github.io/mask2former/].

Detectron2 and PyTorch are required. Other packages include:

    ipython==7.30.1
    numpy==1.21.4
    torch==1.8.1
    torchvision==0.9.1
    opencv-python==4.5.5.62
    Shapely==1.8.0
    h5py==3.6.0
    scipy==1.7.3
    submitit==1.4.1
    scikit-image==0.19.1
    Cython==0.29.27
    timm==0.4.12

Training on Source Domain

An example of training on CityScapes source domain is given below.

python train_net.py --num-gpus 2 --config-file configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml

Inference on Unseen Target Domains

The below lines are the example code to infer on GTA and SYN unseen target domains.

python train_net.py --config-file configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml --eval-only MODEL.WEIGHTS E:/DGtask/DGViT/Mask2Former-main/output_gta/model_final.pth

python train_net.py --config-file configs/cityscapes/semantic-segmentation/swin/maskformer2_swin_base_IN21k_384_bs16_90k.yaml --eval-only MODEL.WEIGHTS E:/DGtask/DGViT/Mask2Former-main/output_syn/model_final.pth

Cite the proposed CMFormer

If you find the proposed CMFormer is useful for domain-generalized urban-scene segmentation, please cite our work as follows:

@inproceedings{bi2024learning,
  title={Learning content-enhanced mask transformer for domain generalized urban-scene segmentation},
  author={Bi, Qi and You, Shaodi and Gevers, Theo},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={2},
  pages={819--827},
  year={2024}
}

Acknowledgement

The development of CMFormer is largely based on Mask2Former [https://bowenc0221.github.io/mask2former/].

The majority of Mask2Former is licensed under a MIT License.

However portions of the project are available under separate license terms: Swin-Transformer-Semantic-Segmentation is licensed under the MIT license, Deformable-DETR is licensed under the Apache-2.0 License.

If you find the proposed CMFormer is useful for domain-generalized urban-scene segmentation, please also cite the asserts from the orginal Mask2Former as follows:

@inproceedings{cheng2021mask2former,
  title={Masked-attention Mask Transformer for Universal Image Segmentation},
  author={Bowen Cheng and Ishan Misra and Alexander G. Schwing and Alexander Kirillov and Rohit Girdhar},
  journal={CVPR},
  year={2022}
}

Contact

For further information or questions, please contact Qi Bi via [email protected] or [email protected].