Papers › Global Context Networks
Global Context Networks
Yue Cao, Jiarui Xu, Stephen Lin, Fangyun Wei, Han Hu
The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies within an image, 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 the non-local network are almost the same for different query positions. 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 replace the one-layer transformation function of the non-local block by a two-layer bottleneck, which further reduces the parameter number considerably. The resulting network element, called the global context (GC) block, effectively models global context in a lightweight manner, allowing it to be applied at multiple layers of a backbone network to form a global context network (GCNet). Experiments show that GCNet generally outperforms NLNet on major benchmarks for various recognition tasks. The code and network configurations are available 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 r4) | AP50 | 67.9 | #50 of 93 | Archive leaderboard | report |
| Instance Segmentation | COCO minival | GCNet (ResNeXt-101 + DCN + cascade + GC r4) | AP75 | 48.4 | #50 of 93 | Archive leaderboard | report |
| Instance Segmentation | COCO minival | GCNet (ResNeXt-101 + DCN + cascade + GC r4) | mask AP | 44.7 | #50 of 93 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | GCNet (ResNeXt-101 + DCN + cascade + GC r4) | AP50 | 68.9 | #42 of 112 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | GCNet (ResNeXt-101 + DCN + cascade + GC r4) | AP75 | 49.6 | #42 of 112 | Archive leaderboard | report |
| Instance Segmentation | COCO test-dev | GCNet (ResNeXt-101 + DCN + cascade + GC r4) | mask AP | 45.4 | #42 of 112 | Archive leaderboard | report |
| Object Detection | COCO minival | GCNet (ResNeXt-101 + DCN + cascade + GC r4) | AP50 | 70.4 | #70 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | GCNet (ResNeXt-101 + DCN + cascade + GC r4) | AP75 | 56.1 | #70 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | GCNet (ResNeXt-101 + DCN + cascade + GC r4) | box AP | 51.8 | #70 of 220 | Archive leaderboard | report |
| Object Detection | COCO test-dev | GCNet (ResNeXt-101 + DCN + cascade + GC r4) | AP50 | 70.9 | #72 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | GCNet (ResNeXt-101 + DCN + cascade + GC r4) | AP75 | 56.9 | #72 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | GCNet (ResNeXt-101 + DCN + cascade + GC r4) | box mAP | 52.3 | #72 of 225 | 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
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