Bounding Box Regression with Uncertainty for Accurate Object Detection
精確物體檢測的不肯定邊界框迴歸——KL損失（解讀）(源論文) 損失模型 KL損失的網絡架構用於評估本地化信心。與兩級檢測網絡的標準Fast R-CNN頭不一樣，咱們的網絡計算標準誤差和邊界框位置，這在咱們的迴歸損失kl損失中被考慮到。git 模型我的理解： 模型的三個分支，分別是：github Class：是圖像的類別 Box：是預測的框 Box std：是預測框的四個座標（左上角和右下角兩個點的
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相關文章
- 1. Bounding Box Regression with Uncertainty for Accurate Object Detection
- 2. 論文閱讀：Bounding Box Regression with Uncertainty for Accurate Object Detection
- 3. softer-nms論文學習詳解(Bounding Box Regression with Uncertainty for Accurate Object Detection)
- 4. RCNN：Rich feature hierarchies for accurate object detection and semantic segmentation
- 5. 【目標檢測】Bounding Box Regression
- 6. Acquistion Location Confidence for accurate object detection
- 7. Bounding Box Regression
- 8. RCNN中的bounding box regression詳解
- 9. Bounding box regression詳解
- 10. 論文解讀：R-CNN《Rich feature hierarchies for accurate object detection and semantic segmentation》