《Deep Self-Taught Learning for Weakly Supervised Object Localization》
本文提出依靠检测器自身不断改进训练样本质量，不断增强检测器性能的一种全新方法，破解弱监督目标检测问题中训练样本质量低的瓶颈。 Weakly Supervised Objection Localization——弱监督目标定位 也就是说我们训练的图像只有针对图像的【注释】，没有包含物体的矩形框，入论文中的图片所示： 可以很显然的看出，这种无矩形框的弱监督学习要比有矩形框的学习 难很多。 本文的算法流
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相关文章
- 1. Deep Self-Taught Learning for Weakly Supervised Object Localization
- 2. Two-Phase Learning for Weakly Supervised Object Localization
- 3. [CVPR2017] Deep Self-Taught Learning for Weakly Supervised Object Localization 论文笔记
- 4. [CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural n...
- 5. [CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural n
- 6. Rethinking the Route Towards Weakly Supervised Object Localization笔记
- 7. 17-WILDCAT-Weakly-Supervised-Learning-of-DeepConvNets
- 8. Thinking in Weakly Supervised Learning
- 9. 【文献阅读】Zigzag Learning for Weakly Supervised Object Detection
- 10. Learning Deep Features for Discriminative Localization