论文阅读笔记 | (IJCAI 2018 Oral) Collaborative Learning for Weakly Supervised Object Detection
论文来自于上海交通大学，点击下载网络 Abstract 训练一个高准确率的检测模型须要大量精细标注的图片数据，其成本很高。本文提出了一种弱监督协同窗习框架，仅使用粗略标签的图片训练目标检测模型，测试结果显示其定位精确率和检测准确率均显著优于目前最早进的方法。app 本论文提出了一种弱监督协同窗习（WSCL）的框架，将弱监督学习网络和强监督学习网络链接成为一个总体网络，经过一致性损失（consist
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相关文章
- 1. 论文阅读笔记 | (IJCAI 2018 Oral) Collaborative Learning for Weakly Supervised Object Detection
- 2. Collaborative Learning for Weakly Supervised Object Detection 算法笔记
- 3. 论文笔记（五） C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
- 4. 【文献阅读】Zigzag Learning for Weakly Supervised Object Detection
- 5. 笔记：论文阅读Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
- 6. Object Instance Mining for Weakly Supervised Object Detection 论文笔记
- 7. 论文解读：Object Instance Mining for Weakly Supervised Object Detection（AAAI 2020）
- 8. 论文笔记（六）MELM：Min-Entropy Latent Model for Weakly Supervised Object Detection
- 9. 论文笔记（七）泛读 ：Weakly Supervised Region Proposal Network and Object Detection
- 10. [CVPR2017] Deep Self-Taught Learning for Weakly Supervised Object Localization 论文笔记