We will provide a complete usage pretrained models for our paper very soon.

Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images

Wuyang Chen*, Ziyu Jiang*, Zhangyang Wang, Kexin Cui, and Xiaoning Qian

In CVPR 2019 (Oral). [Youtube]

Segmentation of ultra-high resolution images is increasingly demanded in a wide range of applications (e.g. urban planning), yet poses significant challenges for algorithm efficiency, in particular considering the (GPU) memory limits.

We propose collaborative Global-Local Networks (GLNet) to effectively preserve both global and local information in a highly memory-efficient manner.

• Memory-efficient: training w. only one 1080Ti and inference w. less than 2GB GPU memory, for ultra-high resolution images of up to 30M pixels.

• High-quality: GLNet outperforms existing segmentation models on ultra-high resolution images.

Inference memory v.s. mIoU on the DeepGlobe dataset.
GLNet (red dots) integrates both global and local information in a compact way, contributing to a well-balanced trade-off between accuracy and memory usage.

Ultra-high resolution Datasets: DeepGlobe, ISIC, Inria Aerial

GLNet: the global and local branch takes downsampled and cropped images, respectively. Deep feature map sharing and feature map regularization enforce our global-local collaboration. The final segmentation is generated by aggregating high-level feature maps from two branches.

Deep feature map sharing: at each layer, feature maps with global context and ones with local fine structures are bidirectionally brought together, contributing to a complete patch-based deep global-local collaboration.

If you use this code for your research, please cite our paper.

@inproceedings{chen2019GLNET,
  title={Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images},
  author={Chen, Wuyang and Jiang, Ziyu and Wang, Zhangyang and Cui, Kexin and Qian, Xiaoning},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2019}
}

We thank Prof. Andrew Jiang and Junru Wu for helping experiments.