Link of the Paper: https://arxiv.org/abs/1706.03762 Motivation: The inherently sequential nature of Recurrent Models precludes parallelization within training examples. Attention mechanisms have become an integral part of compelling sequence modeling…

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…

Link of the Paper: http://papers.nips.cc/paper/4470-im2text-describing-images-using-1-million-captioned-photographs.pdf Main Points: A large novel data set containing images from the web with associated captions written by people, filtered so that th…

Link of the Paper: https://arxiv.org/pdf/1502.03044.pdf Main Points: Encoder-Decoder Framework: Encoder uses a convolutional neural network to extract a set of feature vectors which the authors refer to as annotation vectors. The extractor produces L…

Link of the Paper: https://arxiv.org/pdf/1409.3215.pdf Main Points: Encoder-Decoder Model: Input sequence -> A vector of a fixed dimensionality -> Target sequence. A multilayered  LSTM: The LSTM did not have difficulty on long sentences. Deep LSTMs…

论文链接:https://arxiv.org/pdf/1502.03044.pdf 代码链接:https://github.com/kelvinxu/arctic-captions & https://github.com/yunjey/show-attend-and-tell & https://github.com/jazzsaxmafia/show_attend_and_tell.tensorflow 主要贡献 在这篇文章中,作者将“注意力机制(Attention Mechanism…

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/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/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…