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GCNet

GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

Introduction

Official Repo

Code Snippet

Abstract

The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies, via aggregating query-specific global context to each query position. However, through a rigorous empirical analysis, we have found that the global contexts modeled by non-local network are almost the same for different query positions within an image. In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation. We further observe that this simplified design shares similar structure with Squeeze-Excitation Network (SENet). Hence we unify them into a three-step general framework for global context modeling. Within the general framework, we design a better instantiation, called the global context (GC) block, which is lightweight and can effectively model the global context. The lightweight property allows us to apply it for multiple layers in a backbone network to construct a global context network (GCNet), which generally outperforms both simplified NLNet and SENet on major benchmarks for various recognition tasks. The code and configurations are released at this https URL.

Citation

@inproceedings{cao2019gcnet,
  title={Gcnet: Non-local networks meet squeeze-excitation networks and beyond},
  author={Cao, Yue and Xu, Jiarui and Lin, Stephen and Wei, Fangyun and Hu, Han},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision Workshops},
  pages={0--0},
  year={2019}
}

Results and models

Cityscapes

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) config download
GCNet R-50-D8 512x1024 40000 5.8 3.93 77.69 78.56 config model | log
GCNet R-101-D8 512x1024 40000 9.2 2.61 78.28 79.34 config model | log
GCNet R-50-D8 769x769 40000 6.5 1.67 78.12 80.09 config model | log
GCNet R-101-D8 769x769 40000 10.5 1.13 78.95 80.71 config model | log
GCNet R-50-D8 512x1024 80000 - - 78.48 80.01 config model | log
GCNet R-101-D8 512x1024 80000 - - 79.03 79.84 config model | log
GCNet R-50-D8 769x769 80000 - - 78.68 80.66 config model | log
GCNet R-101-D8 769x769 80000 - - 79.18 80.71 config model | log

ADE20K

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) config download
GCNet R-50-D8 512x512 80000 8.5 23.38 41.47 42.85 config model | log
GCNet R-101-D8 512x512 80000 12 15.20 42.82 44.54 config model | log
GCNet R-50-D8 512x512 160000 - - 42.37 43.52 config model | log
GCNet R-101-D8 512x512 160000 - - 43.69 45.21 config model | log

Pascal VOC 2012 + Aug

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) config download
GCNet R-50-D8 512x512 20000 5.8 23.35 76.42 77.51 config model | log
GCNet R-101-D8 512x512 20000 9.2 14.80 77.41 78.56 config model | log
GCNet R-50-D8 512x512 40000 - - 76.24 77.63 config model | log
GCNet R-101-D8 512x512 40000 - - 77.84 78.59 config model | log