弱监督目标检测算法论文阅读（二）Rethinking the Route Towards Weakly Supervised Object Localization
个人阅读文章的一点理解，有其他不同理解的同学问欢迎评论交流。 这是南京大学一篇CVPR2020的文章，论文提出了一个伪监督目标定位的方法，生成伪grountruth 进行训练。文章分析了现有的目标检测方法的弊病，联合优化定位与分类并不能两者达到最优。文章创造性的提出了把分类任务和定位任务分开，单独进行训练，取得了很好的效果。 Abstract 弱监督对象定位（WSOL）旨在仅使用图像级标签来定位对
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相关文章
- 1. Rethinking the Route Towards Weakly Supervised Object Localization笔记
- 2. 弱监督目标检测算法论文阅读（五）Combinational Class Activation Maps for Weakly Supervised Object Localization
- 3. 《Deep Self-Taught Learning for Weakly Supervised Object Localization》
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- 10. [CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural n...