G-CNN: an Iterative Grid Based Object Detector
| 
62 | 
44 | 
0
Abstract
We introduce G-CNN, an object detection techniquebased on CNNs which works without proposal algorithms.G-CNN starts with a multi-scale grid of fixed boundingboxes. We train a regressor to move and scale elementsof the grid towards objects iteratively. G-CNN models the problem of object detection as finding a path from a fixed grid to boxes tightly surrounding the objects. G-CNN with around 180 boxes in a multi-scale grid performs comparably to Fast R-CNN which uses around 2K bounding boxes generated with a proposal technique. This strategy makesdetection faster by removing the object proposal stage aswell as reducing the number of boxes to be processed.
还没有评论，说两句吧！
热门资源
The Variational S...
Unlike traditional images which do not offer in...
Stratified Strate...
In this paper we introduce Stratified Strategy ...
Learning to learn...
The move from hand-designed features to learned...
A Mathematical Mo...
Direct democracy, where each voter casts one vo...
Learning to Predi...
Much of model-based reinforcement learning invo...
智能在线
400-630-6780
聆听.建议反馈
E-mail: support@tusaishared.com
资源分类
