(Review cs231n) Spatial Localization and Detection（classification and localization）
重在图像的定位和检测的内容。 一张图片中只有一种给定类别标签的对象，定位则是图像中有对象框；再这些类中，每一个训练目标都有一个类和许多的图像内部对应类的位置选框。 猜想的仅是类标签，不如说它们是位置选框。正确的位置选框，代表你的结果很接近分割的准确率。 研究定位的简单有用基础的范式，就是回归。 这张图片经过一系列的处理过程，最终生成四个代表选框大小的实数，有很多不同的参数来描述选框，人们常用的是
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相关文章
- 1. 【CS231n笔记】08 Spatial Localization and Detection
- 2. Localization and Object Detection
- 3. .Net Globalization and Localization
- 4. DL-semantic segmentation/Classification+Localization/Object Detection/Instance Segmentation
- 5. 目标检测分类和定位：Rethinking Classification and Localization for Object Detection
- 6. 《Double-Head RCNN: Rethinking Classification and Localization for Object Detection》论文详解
- 7. 《SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization》
- 8. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
- 9. Online Object Detection and Localization on Stereo Visual SLAM System
- 10. 【OverFeat】《OverFeat：Integrated Recognition, Localization and Detection using Convolutional Networks》