V4d:4d Convolutional Neural Networks For Video-level Representation Learning
本文是一篇ICLR2020的文章 文章地址： http://xxx.itp.ac.cn/abs/2002.07442 Open Review: https://openreview.net/forum?id=SJeLopEYDH Background 对于视频分类、动作识别等任务来说，如何简洁、高效地建模时序信息一直是重要的研究问题。3D-CNN的提出虽然提供了一个时序建模的方案，但是它的运算量一
相关文章
- 1. Learning Convolutional Neural Networks for Graphs
- 2. Understanding Convolutional Neural Networks for NLP
- 3. [转] Understanding Convolutional Neural Networks for NLP
- 4. Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
- 5. MDNet: Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
- 6. Learning Convolutional Neural Networks for Graphs论文笔记
- 7. Bag of Tricks for Convolutional Neural Networks
- 8. Recurrent Convolutional Neural Networks for Text Classification
- 9. Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
- 10. （4）Neural Networks: Representation
- 更多相关文章...
- • Swift for 循环 - Swift 教程
- • Scala for循环 - Scala教程
- • Java Agent入门实战（三）-JVM Attach原理与使用
- • Java Agent入门实战（一）-Instrumentation介绍与使用
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. js中 charCodeAt
- 2. Android中通过ViewHelper.setTranslationY实现View移动控制（NineOldAndroids开源项目）
- 3. 【Android】日常记录：BottomNavigationView自定义样式，修改点击后图片
- 4. maya 文件检查 ui和数据分离 （一）
- 5. eclipse 修改项目的jdk版本
- 6. Android InputMethod设置
- 7. Simulink中Bus Selector出现很多? ? ?
- 8. 【Openfire笔记】启动Mac版Openfire时提示“系统偏好设置错误”
- 9. AutoPLP在偏好标签中的生产与应用
- 10. 数据库关闭的四种方式
欢迎关注本站公众号,获取更多信息
相关文章
- 1. Learning Convolutional Neural Networks for Graphs
- 2. Understanding Convolutional Neural Networks for NLP
- 3. [转] Understanding Convolutional Neural Networks for NLP
- 4. Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
- 5. MDNet: Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
- 6. Learning Convolutional Neural Networks for Graphs论文笔记
- 7. Bag of Tricks for Convolutional Neural Networks
- 8. Recurrent Convolutional Neural Networks for Text Classification
- 9. Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
- 10. （4）Neural Networks: Representation