Bounding Box Regression with Uncertainty for Accurate Object Detection
精确物体检测的不肯定边界框回归——KL损失（解读）(源论文) 损失模型 KL损失的网络架构用于评估本地化信心。与两级检测网络的标准Fast R-CNN头不一样，咱们的网络计算标准误差和边界框位置，这在咱们的回归损失kl损失中被考虑到。git 模型我的理解： 模型的三个分支，分别是：github Class：是图像的类别 Box：是预测的框 Box std：是预测框的四个坐标（左上角和右下角两个点的
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相关文章
- 1. Bounding Box Regression with Uncertainty for Accurate Object Detection
- 2. 论文阅读：Bounding Box Regression with Uncertainty for Accurate Object Detection
- 3. softer-nms论文学习详解(Bounding Box Regression with Uncertainty for Accurate Object Detection)
- 4. RCNN：Rich feature hierarchies for accurate object detection and semantic segmentation
- 5. 【目标检测】Bounding Box Regression
- 6. Acquistion Location Confidence for accurate object detection
- 7. Bounding Box Regression
- 8. RCNN中的bounding box regression详解
- 9. Bounding box regression详解
- 10. 论文解读：R-CNN《Rich feature hierarchies for accurate object detection and semantic segmentation》