Figure 1: In object detection datasets, the ground-truth bounding boxes have inherent ambiguities in some cases. The bounding box regressor is expected to get smaller loss from ambiguous bounding boxes with our KL Loss. (a)(c) The ambiguities introduced by inaccurate labeling. (b) The ambiguities introduced by occlusion. (d) The object boundary itself is ambiguous. It is unclear where the left boundary of the train is because the tree partially occludes it. (better viewed in color) 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 AP 90 by 1.8% and 6.2% respectively, which significantly outperforms previous stateof-the-art bounding box refinement methods. Our code and models are available at github.com/yihui-he/KL-Loss
He et al REF modeled the bounding box as Gaussian distribution for improved localization.
Bounding Box Regression With Uncertainty for Accurate Object Detection
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science