CVPR2020--ATSS： Adaptive Training Sample Selection
Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection ATSS为CVPR2020中的一篇论文，论文题目如上所示，大体意思为通过自适应选择训练样本来弥补基于锚和无锚检测器的差距。因为目前大多数目标检测成果都是在anchor-based的基础上产生的，
相关文章
- 1. AdaptIS: Adaptive Instance Selection Network
- 2. Adaptive Reference Sample Smoothing(RSAF)
- 3. 论文笔记：目标检测正负样本划分方法Adaptive Training Sample Selection (ATSS)原理
- 4. 目标检测论文: Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample
- 5. 【论文笔记】HSIC WIth Small Training Sample Size Using Superpixel-Guided Training Sample Enlargement
- 6. [目标检测]Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample S
- 7. Hyperspectral Band Selection via Adaptive Subspace Partition Strategy
- 8. [Converge] Feature Selection in training of Deep Learning
- 9. MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection 走读
- 10. Machine Learning Course 4 Selection of Model
- 更多相关文章...
- • Docker 容器连接 - Docker教程
- • PHP headers_list() 函数 - PHP参考手册
- • RxJava操作符（三）Filtering
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
- 1. Duang!超快Wi-Fi来袭
- 2. 机器学习-补充03 神经网络之**函数(Activation Function)
- 3. git上开源maven项目部署 多module maven项目（多module maven+redis+tomcat+mysql）后台部署流程学习记录
- 4. ecliple-tomcat部署maven项目方式之一
- 5. eclipse新导入的项目经常可以看到“XX cannot be resolved to a type”的报错信息
- 6. Spark RDD的依赖于DAG的工作原理
- 7. VMware安装CentOS-8教程详解
- 8. YDOOK：Java 项目 Spring 项目导入基本四大 jar 包 导入依赖，怎样在 IDEA 的项目结构中导入 jar 包 导入依赖
- 9. 简单方法使得putty（windows10上）可以免密登录树莓派
- 10. idea怎么用本地maven
欢迎关注本站公众号,获取更多信息
相关文章
- 1. AdaptIS: Adaptive Instance Selection Network
- 2. Adaptive Reference Sample Smoothing(RSAF)
- 3. 论文笔记：目标检测正负样本划分方法Adaptive Training Sample Selection (ATSS)原理
- 4. 目标检测论文: Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample
- 5. 【论文笔记】HSIC WIth Small Training Sample Size Using Superpixel-Guided Training Sample Enlargement
- 6. [目标检测]Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample S
- 7. Hyperspectral Band Selection via Adaptive Subspace Partition Strategy
- 8. [Converge] Feature Selection in training of Deep Learning
- 9. MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection 走读
- 10. Machine Learning Course 4 Selection of Model