DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation

This project hosts the code for implementing the DCT-MASK algorithms for instance segmentation.

[DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation] Xing Shen*, Jirui Yang*, Chunbo Wei, Bing Deng, Jianqiang Huang, Xiansheng Hua Xiaoliang Cheng, Kewei Liang

In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition(CVPR 2021)

arXiv preprint(arXiv:2011.09876)

Contributions

• We propose a high-quality and low-complexity mask representation for instance segmentation, which encodes the high-resolution binary mask into a compact vector with discrete cosine transform.
• With slight modifications, DCT-Mask could be integrated into most pixel-based frameworks, and achieve significant and consistent improvement on different datasets, backbones, and training schedules. Specifically, it obtains more improvements for more complex backbones and higher-quality annotations.
• DCT-Mask does not require extra pre-processing or pre-training. It achieves high-resolution mask prediction at a speed similar to low-resolution.

Installation

Requirements

• PyTorch ≥ 1.5 and fvcore == 0.1.1.post20200716

This implementation is based on detectron2. Please refer to INSTALL.md. for installation and dataset preparation.

Usage

The codes of this project is on projects/DCT_Mask/

Train with multiple GPUs

cd ./projects/DCT_Mask/
./train1.sh

Testing

cd ./projects/DCT_Mask/
./test1.sh

Model ZOO

Trained models on COCO

Trained models on Cityscapes

Notes

• We observe about 0.2 AP noise in COCO.
• High variance observed in CityScapes when trained on fine annotations only. We report the median of 5 runs AP in the article (i.e. 35.6), while in this repo we report the best results (37.0).
• Initialized from COCO pre-training will reduce the variance on CityScapes as well as increasing mask AP.
• The inference time is measured on single GPU with batchsize 1. All GPUs are NVIDIA V100.
• Lvis 0.5 is used for evaluation.

Contributing to the project

Any pull requests or issues are welcome.

If there is any problem with this project, please contact Xing Shen.

Citations

Please consider citing our papers in your publications if the project helps your research.

License

• MIT License.