# KL-Divergence-Based Region Proposal Network for Object Detection

arXiv: https://arxiv.org/abs/2005.11220 · HTML: https://mlsanity.com/paper/2005.11220/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2020.

## Built on by (most cited first, 4 of 4)

- [Compressed Meta-Optical Encoder for Image Classification](https://mlsanity.com/paper/2406.06534/) (2024)
- [Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer](https://mlsanity.com/paper/2407.08460/) (2024)
- [Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation](https://mlsanity.com/paper/2111.06500/) (2021)
- [Transferable polychromatic optical encoder for neural networks](https://mlsanity.com/paper/2411.02697/) (2024)

## Abstract (arXiv)

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.