Papers › GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, Han Hu
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 https://github.com/xvjiarui/GCNet.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Instance Segmentation | COCO minival | GCNet (ResNeXt-101 + DCN + cascade + GC r16) | mask AP | 40.9 | #70 of 93 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | GCNet (ResNeXt-101 + DCN + cascade + GC r16) | mask AP | 41.5% | #60 of 112 | Archive leaderboard | report |
| Object Detection | COCO minival | GCNet (ResNeXt-101 + DCN + cascade + GC r16) | AP50 | 66.9 | #93 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | GCNet (ResNeXt-101 + DCN + cascade + GC r16) | AP75 | 52.2 | #93 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | GCNet (ResNeXt-101 + DCN + cascade + GC r16) | box AP | 47.9 | #93 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | GCnet (ResNet-50-FPN, GRoIE) | AP50 | 62.4 | #178 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | GCnet (ResNet-50-FPN, GRoIE) | AP75 | 44 | #178 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | GCnet (ResNet-50-FPN, GRoIE) | APL | 52.5 | #178 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | GCnet (ResNet-50-FPN, GRoIE) | APM | 44.4 | #178 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | GCnet (ResNet-50-FPN, GRoIE) | APS | 24.2 | #178 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | GCnet (ResNet-50-FPN, GRoIE) | box AP | 40.3 | #178 of 220 | Archive leaderboard | report |
| Object Detection | COCO test-dev | GCNet (ResNeXt-101 + DCN + cascade + GC r4) | AP50 | 67.6 | #106 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | GCNet (ResNeXt-101 + DCN + cascade + GC r4) | AP75 | 52.7 | #106 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | GCNet (ResNeXt-101 + DCN + cascade + GC r4) | Operations per network pass | 54.8G | #106 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | GCNet (ResNeXt-101 + DCN + cascade + GC r4) | box mAP | 48.4 | #106 of 225 | Archive leaderboard | report |
| Object Detection | COCO-O | GCNet (RX-101-32x4d-DCN) | Average mAP | 26.0 | #25 of 45 | Archive leaderboard | report |
| Object Detection | COCO-O | GCNet (RX-101-32x4d-DCN) | Effective Robustness | 4.38 | #25 of 45 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: GCNet, Global Context Block
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