[RCNN]Rich feature hierarchies for accurate object detection and semantic segmentation
思想 本文解決了目標檢測中的兩個關鍵問題。 問題一：速度 經典的目標檢測算法使用滑動窗法依次判斷所有可能的區域。本文則預先提取一系列較可能是物體的候選區域，之後僅在這些候選區域上提取特徵，進行判斷。 問題二：訓練集 經典的目標檢測算法在區域中提取人工設定的特徵（Haar，HOG）。本文則需要訓練深度網絡進行特徵提取。可供使用的有兩個數據庫： 一個較大的識別庫（ImageNet ILSVC 201
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相關文章
- 1. RCNN：Rich feature hierarchies for accurate object detection and semantic segmentation
- 2. Rich feature hierarchies for accurate object detection and semantic segmentation
- 3. 目標檢測--Rich feature hierarchies for accurate object detection and semantic segmentation(CVPR 2014)
- 4. RCNN學習筆記(1):Rich feature hierarchies for accurate object detection and semantic segmentation
- 5. 論文學習《Rich feature hierarchies for accurate object detection and semantic segmentation》
- 6. R-CNN:Rich feature hierarchies for accurate object detection and semantic segmentation
- 7. (38)[CVPR14] R-CNN: Rich feature hierarchies for accurate object detection and semantic segmentation
- 8. 【論文閱讀】Rich feature hierarchies for accurate object detection and semantic segmentation
- 9. R-CNN論文翻譯Rich feature hierarchies for accurate object detection and semantic segmentation
- 10. 論文筆記：Rich feature hierarchies for accurate object detection and semantic segmentation