【论文学习】Rich feature hierarchies for accurate object detection and semantic segmentation
论文地址：R-CNN 1. Abstract 在经典的PASCAL VOC数据集测试的目标检测的性能在近些年中逐渐趋于稳定。其中性能最好的方法是复合系统，通常把多个低级的图像特征和高级的上下文组合在一起。本文中，作者提出了一种简单且可扩展的检测算法，提升了在VOC 2012数据集上30%的mAP，达到了53.3%。其主要的想法有两个：1.把大容量的卷积神经网络应用在自下而上的region prop
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相关文章
- 1. Rich feature hierarchies for accurate object detection and semantic segmentation
- 2. 论文学习《Rich feature hierarchies for accurate object detection and semantic segmentation》
- 3. 【论文阅读】Rich feature hierarchies for accurate object detection and semantic segmentation
- 4. R-CNN论文翻译Rich feature hierarchies for accurate object detection and semantic segmentation
- 5. 论文笔记：Rich feature hierarchies for accurate object detection and semantic segmentation
- 6. 【论文快读】Rich feature hierarchies for accurate object detection and semantic segmentation
- 7. Rich feature hierarchies for accurate object detection and semantic segmentation论文细读
- 8. 【论文阅读笔记】Rich feature hierarchies for accurate object detection and semantic segmentation
- 9. R-CNN（Rich feature hierarchies for accurate object detection and semantic segmentation）论文理解...
- 10. 论文：Rich feature hierarchies for accurate object detection and semantic segmentation 个人笔记