Grid R-CNN

CVPR 2019  ·  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. read more

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Object Detection COCO minival Grid R-CNN (ResNet-101-FPN) box AP 41.3 # 69
AP50 60.3 # 51
AP75 44.4 # 46
APS 23.4 # 51
APM 45.8 # 35
APL 54.1 # 47
Object Detection COCO minival Grid R-CNN (ResNet-50-FPN) box AP 39.6 # 89
AP50 58.3 # 68
AP75 42.4 # 57
APS 22.6 # 56
APM 43.8 # 47
APL 51.5 # 58
Object Detection COCO test-dev Grid R-CNN (ResNeXt-101-FPN) box AP 43.2 # 102
AP50 63.0 # 91
AP75 46.6 # 92
APS 25.1 # 90
APM 46.5 # 90
APL 55.2 # 92

Methods