BING: Binarized Normed Gradients for Objectness Estimation at 300fps

时间 2021-01-21

标签 bing objectness cvpr2014 繁體版

原文原文链接

BING: Binarized Normed Gradients for Objectness Estimation at 300fps 基于二值化赋范梯度特征的一般对象估计摘要：训练通用对象度量来生成一小组候选对象窗口，已经显示出加速了经典的滑动窗口对象检测范式。我们观察到，通过观察梯度的标准，可以通过将其对应的图像窗口适当地调整到小的固定尺寸来区分具有良好定义的闭合边界的通用对象。基于

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1. 论文笔记BING（ Binarized normed gradients）与BING-E
2. CVPR2014 Objectness BING 源码详解
3. [ICCV 17 oral] Binarized Convolutional Landmark Localizers for Human Pose Estimation
4. 二值人脸对齐--Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment
5. 图像显著性检测论文及代码汇总
6. 转---图像显著性检测论文及代码汇总
7. Histograms of Oriented Gradients for Human Detection
8. RON: Reverse Connection with Objectness Prior Networks for Object Detection
9. Dense Scene Information Estimation Network for Dehazing_CVPR2019
10. cs231n_2018_lecture11_notes_rnn视觉应用

>>更多相关文章<<