JavaShuo
栏目
标签
《Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models》
时间 2021-01-02
原文
原文链接
来源:CVPR2018 一、Introduction 第一篇同时利用GAN和Reinforcement Learning(RL)做跨媒体检索的文章。 这个网络可以同时做三个跨媒体的任务:cross-media retrieval,image caption and text-to-image synthesis(对于后两个任务,文章只给出了可视化的结果,没有给出定量的分析)。 这篇文章发表在CVP
>>阅读原文<<
相关文章
1.
Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models
2.
IMPROVING GENERATIVE ADVERSARIAL NETWORKS WITH DENOISING FEATURE MATCHING(Bingio-ICLR2017)
3.
Beyond Part Models- Person Retrieval with Refined Part Pooling
4.
Beyond Part Models: Person Retrieval with Refined Part Pooling (ECCV2018)
5.
【论文精读】Improving Simple Models with Confidence Profiles
6.
【ReID】Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional...
7.
Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)
8.
Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)
9.
DCGAN应用: Semantic Image Inpainting with Deep Generative Models
10.
GAUSSIAN MIXTURE VAE: LESSONS IN VARIATIONAL INFERENCE, GENERATIVE MODELS, AND DEEP NETS
更多相关文章...
•
XSL-FO table-and-caption 对象
-
XSL-FO 教程
•
W3C RDF and OWL 活动
-
W3C 教程
•
RxJava操作符(七)Conditional and Boolean
•
算法总结-股票买卖
相关标签/搜索
look
generative
improving
imagine
retrieval
match
models
index+match
models&orm
2.models
0
分享到微博
分享到微信
分享到QQ
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.
android 以太网和wifi共存
2.
没那么神秘,三分钟学会人工智能
3.
k8s 如何 Failover?- 每天5分钟玩转 Docker 容器技术(127)
4.
安装mysql时一直卡在starting the server这一位置,解决方案
5.
秋招总结指南之“性能调优”:MySQL+Tomcat+JVM,还怕面试官的轰炸?
6.
布隆过滤器了解
7.
深入lambda表达式,从入门到放弃
8.
中间件-Nginx从入门到放弃。
9.
BAT必备500道面试题:设计模式+开源框架+并发编程+微服务等免费领取!
10.
求职面试宝典:从面试官的角度,给你分享一些面试经验
本站公众号
欢迎关注本站公众号,获取更多信息
相关文章
1.
Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models
2.
IMPROVING GENERATIVE ADVERSARIAL NETWORKS WITH DENOISING FEATURE MATCHING(Bingio-ICLR2017)
3.
Beyond Part Models- Person Retrieval with Refined Part Pooling
4.
Beyond Part Models: Person Retrieval with Refined Part Pooling (ECCV2018)
5.
【论文精读】Improving Simple Models with Confidence Profiles
6.
【ReID】Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional...
7.
Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)
8.
Beyond Part Models: Person Retrieval with Refined Part Pooling (and A Strong Convolutional Baseline)
9.
DCGAN应用: Semantic Image Inpainting with Deep Generative Models
10.
GAUSSIAN MIXTURE VAE: LESSONS IN VARIATIONAL INFERENCE, GENERATIVE MODELS, AND DEEP NETS
>>更多相关文章<<