<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[jiang6869732的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/jiang6869732</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; jiang6869732]]></copyright><item><title><![CDATA[《Multi-level Attention Networks for Visual Question Answering》阅读笔记]]></title><link>https://blog.youkuaiyun.com/jiang6869732/article/details/81532197</link><guid>https://blog.youkuaiyun.com/jiang6869732/article/details/81532197</guid><author>jiang6869732</author><pubDate>Thu, 09 Aug 2018 10:36:42 +0800</pubDate><description><![CDATA[《Multi-level Attention Networks for Visual Question Answering》阅读笔记



一、研究背景


effective semantic embedding and fine-grained visual understanding；
人类语言问题以明确的查询意图传达强大的高级语义，而具有数万个像素的真实世界图像则相对低级且抽象，由于众所周...]]></description><category></category></item><item><title><![CDATA[转载《Fast R-CNN》的相关原理及解释]]></title><link>https://blog.youkuaiyun.com/jiang6869732/article/details/81453952</link><guid>https://blog.youkuaiyun.com/jiang6869732/article/details/81453952</guid><author>jiang6869732</author><pubDate>Mon, 06 Aug 2018 14:38:26 +0800</pubDate><description><![CDATA[转载《Fast R-CNN》的相关原理及解释

Fast R-CNN的相关原理及解释]]></description><category></category></item><item><title><![CDATA[《Visual Question Generation as Dual Task of Visual Question Answering》阅读笔记]]></title><link>https://blog.youkuaiyun.com/jiang6869732/article/details/81412426</link><guid>https://blog.youkuaiyun.com/jiang6869732/article/details/81412426</guid><author>jiang6869732</author><pubDate>Sat, 04 Aug 2018 16:31:43 +0800</pubDate><description><![CDATA[《Visual Question Generation as Dual Task of Visual Question Answering》阅读笔记



一、研究背景

视觉问答（VQA）和视觉问题生成（VQG）是计算机视觉中的两个主题，但它们通常是分开探索的，尽管它们具有内在的互补关系。 在本文中，提出了一个端到端的统一模型，即可逆问答网络（iQAN），将问题生成作为问题回答的双重任务来引入，...]]></description><category></category></item><item><title><![CDATA[《MUTAN: Multimodal Tucker Fusion for Visual Question Answering》阅读笔记]]></title><link>https://blog.youkuaiyun.com/jiang6869732/article/details/81409668</link><guid>https://blog.youkuaiyun.com/jiang6869732/article/details/81409668</guid><author>jiang6869732</author><pubDate>Sat, 04 Aug 2018 12:26:06 +0800</pubDate><description><![CDATA[《MUTAN: Multimodal Tucker Fusion for Visual Question Answering》阅读笔记

一、研究背景

Bilinear models在视觉问答（VQA）任务中进行信息融合提供了一个吸引人的框架。 它们有助于学习question meaning 和 visual concepts in the image之间的高层次关系，但它们存在高维度问题。 论...]]></description><category></category></item><item><title><![CDATA[《iVQA: Inverse Visual Question Answering》读书笔记]]></title><link>https://blog.youkuaiyun.com/jiang6869732/article/details/81392761</link><guid>https://blog.youkuaiyun.com/jiang6869732/article/details/81392761</guid><author>jiang6869732</author><pubDate>Fri, 03 Aug 2018 18:43:33 +0800</pubDate><description><![CDATA[《iVQA: Inverse Visual Question Answering》读书笔记

一、iVQA介绍 
VQA是根据image、question生成关于image的答案，而iVQA是VQA的逆过程，给出一个answer、image生成与之相关的question。 
二、iVQA面临的挑战

（1） iVQA模型利用问题偏差的可能性可能小于VQA通过答案偏差得分高的范围（问题偏差较少，利用...]]></description><category></category></item></channel></rss>