Papers › Grid R-CNN
Grid R-CNN
Xin Lu, Buyu Li, Yuxin Yue, Quanquan Li, Junjie Yan
This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection. Different from the traditional regression based methods, the Grid R-CNN captures the spatial information explicitly and enjoys the position sensitive property of fully convolutional architecture. Instead of using only two independent points, we design a multi-point supervision formulation to encode more clues in order to reduce the impact of inaccurate prediction of specific points. To take the full advantage of the correlation of points in a grid, we propose a two-stage information fusion strategy to fuse feature maps of neighbor grid points. The grid guided localization approach is easy to be extended to different state-of-the-art detection frameworks. Grid R-CNN leads to high quality object localization, and experiments demonstrate that it achieves a 4.1% AP gain at IoU=0.8 and a 10.0% AP gain at IoU=0.9 on COCO benchmark compared to Faster R-CNN with Res50 backbone and FPN architecture.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 2D Object Detection | SARDet-100K | Grid RCNN | box mAP | 48.8 | #10 of 13 | Archive leaderboard | report |
| Object Detection | COCO minival | Grid R-CNN (ResNet-101-FPN) | AP50 | 60.3 | #165 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Grid R-CNN (ResNet-101-FPN) | AP75 | 44.4 | #165 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Grid R-CNN (ResNet-101-FPN) | APL | 54.1 | #165 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Grid R-CNN (ResNet-101-FPN) | APM | 45.8 | #165 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Grid R-CNN (ResNet-101-FPN) | APS | 23.4 | #165 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Grid R-CNN (ResNet-101-FPN) | box AP | 41.3 | #165 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Grid R-CNN (ResNet-50-FPN) | AP50 | 58.3 | #187 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Grid R-CNN (ResNet-50-FPN) | AP75 | 42.4 | #187 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Grid R-CNN (ResNet-50-FPN) | APL | 51.5 | #187 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Grid R-CNN (ResNet-50-FPN) | APM | 43.8 | #187 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Grid R-CNN (ResNet-50-FPN) | APS | 22.6 | #187 of 220 | Archive leaderboard | report |
| Object Detection | COCO minival | Grid R-CNN (ResNet-50-FPN) | box AP | 39.6 | #187 of 220 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Grid R-CNN (ResNeXt-101-FPN) | AP50 | 63.0 | #160 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Grid R-CNN (ResNeXt-101-FPN) | AP75 | 46.6 | #160 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Grid R-CNN (ResNeXt-101-FPN) | APL | 55.2 | #160 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Grid R-CNN (ResNeXt-101-FPN) | APM | 46.5 | #160 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Grid R-CNN (ResNeXt-101-FPN) | APS | 25.1 | #160 of 225 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Grid R-CNN (ResNeXt-101-FPN) | box mAP | 43.2 | #160 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
Introduced by this paper: Grid R-CNN
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