Rethinking the Route Towards Weakly Supervised Object Localization笔记
CVPR2020 本文提出伪监督目标定位方法（PSOL）解决弱监督目标定位问题。将定位与分类分为两个独立的网络，在训练集上使用DDT生成伪ground truth，通过回归与真实GT box对比， 整体效果达到SOTA。在不同数据集上，PSOL方法不需要fine-tuning也具有很好的定位能力，迁移能力。 由于训练集数据大量标注困难，成本高。很多开始研究如何使用弱监督方法来学习 目标定位。弱监督
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- 1. 弱监督目标检测算法论文阅读（二）Rethinking the Route Towards Weakly Supervised Object Localization
- 2. Two-Phase Learning for Weakly Supervised Object Localization
- 3. Deep Self-Taught Learning for Weakly Supervised Object Localization
- 4. 《Deep Self-Taught Learning for Weakly Supervised Object Localization》
- 5. [CVPR2017] Deep Self-Taught Learning for Weakly Supervised Object Localization 论文笔记
- 6. [CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural n...
- 7. [CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural n
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- 10. Weakly-Supervised Action Localization by Generative Attention Modeling批注