Grid R-CNN

2000년 도입 · 논문 4편에서 사용

Grid R-CNN is an object detection framework, where the traditional regression formulation is replaced by a grid point guided localization mechanism. Grid R-CNN divides the object bounding box region into grids and employs a fully convolutional network (FCN) to predict the locations of grid points. Owing to the position sensitive property of fully convolutional architecture, Grid R-CNN maintains the explicit spatial information and grid points locations can be obtained in pixel level. When a certain number of grid points at specified location are known, the corresponding bounding box is definitely determined. Guided by the grid points, Grid R-CNN can determine more accurate object bounding box than regression method which lacks the guidance of explicit spatial information.

출처: Grid R-CNN

소개 논문: Grid R-CNN