GLNet for Memory-Efficient Segmentation of Ultra-High Resolution Images
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).
Overview
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.
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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.
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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
Methods
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.
Citation
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}
}
Acknowledge
The work of Z. Wang is in part supported by the National Science Foundation Award RI-1755701. The work of X.Qian is in part supported by the National Science Foundation Award CCF-1553281. We also thank Prof. Andrew Jiang and Junru Wu for helping experiments.