Methods > Computer Vision > Object Detection Models

A Global Context Network, or GCNet, utilises global context blocks to model long-range dependencies in images. It is based on the Non-Local Network, but it modifies the architecture so less computation is required. Global context blocks are applied to multiple layers in a backbone network to construct the GCNet.

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

Latest Papers

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Global Correlation Network: End-to-End Joint Multi-Object Detection and Tracking
Xuewu LinYu-ang GuoJianqiang Wang
2021-03-23
Bilateral Grid Learning for Stereo Matching Networks
Bin XuYuhua XuXiaoli YangWei JiaYulan Guo
2021-01-01
Global Context Networks
| Yue CaoJiarui XuStephen LinFangyun WeiHan Hu
2020-12-24
Do End-to-end Stereo Algorithms Under-utilize Information?
| Changjiang CaiPhilippos Mordohai
2020-10-14
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
| Yue CaoJiarui XuStephen LinFangyun WeiHan Hu
2019-04-25

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