Name
Abstract | ||
|---|---|---|
The learning of the region proposal in object detection using the deep neural networks (DNN) is divided into two tasks: binary classification and bounding box regression task. However, traditional RPN (Region Proposal Network) defines these two tasks as different problems, and they are trained independently. In this paper, we propose a new region proposal learning method that considers the bounding box offset's uncertainty in the objectness score. Our method redefines RPN to a problem of minimizing the KL-divergence, difference between the two probability distributions. We applied KL-RPN, which performs region proposal using KL-Divergence, to the existing two-stage object detection framework and showed that it can improve the performance of the existing method. Experiments show that it achieves 2.6% and 2.0% AP improvements on MS COCO test-dev in Faster R-CNN with VGG-16 and R-FCN with ResNet-101 backbone, respectively. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICIP40778.2020.9191075 | 2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
Keywords | DocType | ISSN |
Object Detection, Neural Network, Deep Learning, KL-Divergence | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seo Geonseok | 1 | 0 | 0.34 |
| Jaeyoung Yoo | 2 | 1 | 3.08 |
| Choi Jaeseok | 3 | 0 | 0.34 |
| Nojun Kwak | 4 | 862 | 63.79 |