Paper Reading - Convolutional Image Captioning ( CVPR 2018 )

Innovations:

The authors develop a convolutional ( CNN-based ) image captioning method that shows comparable performance to an LSTM based method on standard metrics.
The authors analyze the characteristics of CNN and LSTM nets and provide useful insights such as -- CNNs produce more entropy ( useful for diverse predictions ), better classification accuracy, and do not suffer from vanishing gradients.

Improvements:

A Convolutional Neural Network with Attention mechanism.

General Points:

Image Captioning is applicable to virtual assistants, editing tools, image indexing and support of the disabled.
Image Captioning is a basic ingredient for more complex operations such as storytelling and visual summarization.

原文地址：https://www.cnblogs.com/zlian2016/p/9520893.html

时间： 2024-08-02 02:44:17

Paper Reading - Convolutional Image Captioning ( CVPR 2018 )的相关文章

单目标跟踪CVPR 2018 ECO+

FROM https://blog.csdn.net/weixin_40245131/article/details/79754531 目标跟踪的相关滤波方向,Martin Danelljan 4月底在arXiv上挂出来的最新论文: Bhat G, Johnander J, Danelljan M, et al. Unveiling the Power of Deep Tracking [J]. arXiv preprint arXiv:1804.06833, 2018. https://arx

爬取CVPR 2018过程中遇到的坑

爬取 CVPR 2018 过程中遇到的坑使用语言及模块语言: Python 3.6.6 模块: re requests lxml bs4 过程一开始都挺顺利的,先获取到所有文章的链接再逐个爬取获取内容, 中间有一部分的是用正则进行匹配出想要的内容,写完了就想全部跑一遍试试吧. 爬到一半出错了,看了一下是这篇出问题了. 好吧,那就f12看看什么情况. emmmmm.... 跟之前的差不多啊... 直接复制下来匹配试试 ...都能匹配到啊... 直到....emmmm....看看不print出

Paper Reading: Stereo DSO

开篇第一篇就写一个paper reading吧,用markdown+vim写东西切换中英文挺麻烦的,有些就偷懒都用英文写了. Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras Abstract Optimization objectives: intrinsic/extrinsic parameters of all keyframes all selected pixels' depth Inte

CVPR 2016 paper reading (6)

1. Neuroaesthetics in fashion: modeling the perception of fashionability, Edgar Simo-Serra, Sanja Fidler, Francesc Moreno-Noguer, Raquel Urtasun, in CVPR 2015. Goal: learn and predict how fashionable a person looks on a photograph, and suggest subtle

CVPR 2018 | 腾讯AI Lab入选21篇论文详解

近十年来在国际计算机视觉领域最具影响力.研究内容最全面的顶级学术会议CVPR,近日揭晓2018年收录论文名单,腾讯AI Lab共有21篇论文入选,位居国内企业前列,我们将在下文进行详解,欢迎交流与讨论. 去年CVPR的论文录取率为29%,腾讯AI Lab 共有6篇论文入选,点击这里可以回顾.2017年,腾讯 AI Lab共有100多篇论文发表在AI顶级会议上,包括ICML(4篇).ACL(3篇).NIPS(8篇)等. 我们还坚持与学界.企业界和行业「共享AI+未来」,已与美国麻省理工大学.英国

【Paper Reading】R-CNN（V5）论文解读

R-CNN论文:Rich feature hierarchies for accurate object detection and semantic segmentation 用于精确目标检测和语义分割的丰富特征层次结构作者:Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik,UC Berkeley(加州大学伯克利分校)一作者Ross Girshick的个人首页:http://www.rossgirshick.info/,有其

Paper Reading: Perceptual Generative Adversarial Networks for Small Object Detection

Perceptual Generative Adversarial Networks for Small Object Detection 2017-07-11 19:47:46 CVPR 2017 This paper use GAN to handle the issue of small object detection which is a very hard problem in general object detection. As shown in the followin

Paper Reading: Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual TrackingECCV 2016 The key point of KCF is the ability to efficiently exploit available negative data by including all shifted versions of a training sample, in anthor w

Paper Reading: In Defense of the Triplet Loss for Person Re-Identification

In Defense of the Triplet Loss for Person Re-Identification 2017-07-02 14:04:20 This blog comes from: http://blog.csdn.net/shuzfan/article/details/70069822 Paper: https://arxiv.org/abs/1703.07737 Github: https://github.com/VisualComputingInstitu