CVPR 2019 Open Access Repository
CVPR 2019 [presentation (youtube)]
Yihui He, Chenchen Zhu, Jianren Wang, Marios Savvides, Xiangyu Zhang, Carnegie Mellon University & Megvii Inc.
https://www.youtube.com/embed/bcGtNdTzdkc
Large-scale object detection datasets (e.g., MS-COCO) try to define the ground truth bounding boxes as clear as possible. However, we observe that ambiguities are still introduced when labeling the bounding boxes. In this paper, we propose a novel bounding box regression loss for learning bounding box transformation and localization variance together. Our loss greatly improves the localization accuracies of various architectures with nearly no additional computation. The learned localization variance allows us to merge neighboring bounding boxes during non-maximum suppression (NMS), which further improves the localization performance. On MS-COCO, we boost the Average Precision (AP) of VGG-16 Faster R-CNN from 23.6% to 29.1%. More importantly, for ResNet-50-FPN Mask R-CNN, our method improves the AP and AP90 by 1.8% and 6.2% respectively, which significantly outperforms previous state-of-the-art bounding box refinement methods.
If you find the code useful in your research, please consider citing:
@InProceedings{klloss,
author = {He, Yihui and Zhu, Chenchen and Wang, Jianren and Savvides, Marios and Zhang, Xiangyu},
title = {Bounding Box Regression With Uncertainty for Accurate Object Detection},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
Please find installation instructions for Caffe2 and Detectron in INSTALL.md.
When installing cocoapi, please use my fork to get AP80 and AP90 scores.