Figure 1: When curb ramps (green rectangle) are missing from a segment of sidewalks in an intersection (orange rectangle), people with mobility impairments are unable to cross the street. We propose an approach to determine where objects are missing by learning a context model so that it can be combined with object detection results.  Abstract Most of computer vision focuses on what is in an im
In addition, Sun and Jacobs REF propose to learn a context model that predicts where objects may be missing.
Seeing What is Not There: Learning Context to Determine Where Objects are Missing
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science