Link of the Paper: https://arxiv.org/abs/1711.09151 Motivation: LSTM units are complex and inherently sequential across time. Convolutional networks have shown advantages on machine translation and conditional image generation. Innovation: The author…

Convolutional Image Captioning 2018-11-04 20:42:07 Paper: http://openaccess.thecvf.com/content_cvpr_2018/papers/Aneja_Convolutional_Image_Captioning_CVPR_2018_paper.pdf Code: https://github.com/aditya12agd5/convcap Related Papers: 1. Convolutional Se…

Link of the Paper: https://arxiv.org/abs/1806.06422 Innovations: The authors propose a novel learning based discriminative evaluation metric that is directly trained to distinguish between human and machine-generated captions. They train an automatic…

Link of the Paper: https://arxiv.org/abs/1705.03122 Motivation: Compared to recurrent layers, convolutions create representations for fixed size contexts, however, the effective context size of the network can easily be made larger by stacking severa…

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…

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…

这是 CVPR 2018 的一篇少样本学习论文:Learning to Compare: Relation Network for Few-Shot Learning 源码地址:https://github.com/floodsung/LearningToCompare_FSL 环境选用 Tensorflow 1.4 因为他是 cuda8 的. 切换conda源 bash /public/script/switch_conda_source.sh 创建虚拟python环境 conda creat…

Link of the Paper: https://arxiv.org/abs/1411.4389 Main Points: A novel Recurrent Convolutional Architecture ( CNN + LSTM ): both Spatially and Temporally Deep. The recurrent long-term models are directly connected to modern visual convnet models and…

Link of the Paper: https://arxiv.org/abs/1805.09019 Innovations: The authors propose a CNN + CNN framework for image captioning. There are four modules in the framework: vision module ( VGG-16 ), which is adopted to "watch" images; language modu…

Link of the Paper: https://arxiv.org/pdf/1412.6632.pdf Main Points: The authors propose a multimodal Recurrent Neural Networks ( AlexNet/VGGNet + a multimodal layer + RNNs ). Their work has two major differences from these methods. Firstly, they inco…