[RCNN]Rich feature hierarchies for accurate object detection and semantic segmentation
思想 本文解决了目标检测中的两个关键问题。 问题一：速度 经典的目标检测算法使用滑动窗法依次判断所有可能的区域。本文则预先提取一系列较可能是物体的候选区域，之后仅在这些候选区域上提取特征，进行判断。 问题二：训练集 经典的目标检测算法在区域中提取人工设定的特征（Haar，HOG）。本文则需要训练深度网络进行特征提取。可供使用的有两个数据库： 一个较大的识别库（ImageNet ILSVC 201
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相关文章
- 1. RCNN：Rich feature hierarchies for accurate object detection and semantic segmentation
- 2. Rich feature hierarchies for accurate object detection and semantic segmentation
- 3. 目标检测--Rich feature hierarchies for accurate object detection and semantic segmentation(CVPR 2014)
- 4. RCNN学习笔记(1):Rich feature hierarchies for accurate object detection and semantic segmentation
- 5. 论文学习《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. 【论文阅读】Rich feature hierarchies for accurate object detection and semantic segmentation
- 9. R-CNN论文翻译Rich feature hierarchies for accurate object detection and semantic segmentation
- 10. 论文笔记：Rich feature hierarchies for accurate object detection and semantic segmentation