论文笔记：Learning Region Features for Object Detection
来源：paper, MSRA & PKU, ECCV 2018 一些讲解 作者讲解 作者认为数据驱动的方式讷讷够提高准确性并且带来更强的适应性，即将原来人工智能系统各个模块中手工设置的部分改由可学习的参数。本文主要针对特征提取部分，例如ROI Pooling方法。 区域特征提取的一般化视角 基于区域的物体检测方法步骤总结为：图像特征生成、候选区域生成、区域特征提取、区域识别和重复检测去除。在区域特
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相关文章
- 1. 论文笔记：Learning Region Features for Object Detection
- 2. Learning Region Features for Object Detection
- 3. 【论文笔记】：Learning Rich Features at High-Speed for Single-Shot Object Detection
- 4. Learning Rich Features at High-Speed for Single-Shot Object Detection 论文笔记
- 5. Richer Convolutional Features for Edge Detection论文笔记
- 6. 论文笔记 Deep Learning for Generic Object Detection: A Survey （一）
- 7. 论文笔记：Relation Networks for Object Detection
- 8. 【论文笔记】：Overlap Sampler for Region-Based Object Detection
- 9. 论文解析——Deep Reinforcement Learning of Region Proposal Networks for Object Detection
- 10. Learning Deep Features for Discriminative Localization论文笔记