摘要: Learning to Read Chest X-Rays:Recurrent Neural Cascade Model for Automated Image Annotation (CVPR 2016) Goals: -Learn to read chest x-rays from an exi 阅读全文
posted @ 2018-11-30 21:58 一窍不通 阅读(1751) 评论(0) 推荐(0) 编辑
摘要: Image Caption: Automatically describing the content of an image domain:CV+NLP Category:(by myself, you can read the survey for detail.) CNN+RNN, with 阅读全文
posted @ 2018-11-02 17:50 一窍不通 阅读(4978) 评论(0) 推荐(0) 编辑
摘要: LSTM: http://colah.github.io/posts/2015-08-Understanding-LSTMs/ 深度学习领域PyTorch项目-git源码整理 https://blog.csdn.net/u012969412/article/details/77479269?utm_ 阅读全文
posted @ 2018-10-20 21:45 一窍不通 阅读(751) 评论(0) 推荐(0) 编辑
摘要: Neural Storyteller (Krios et al. 2015) : NST breaks down the task into two steps, which first generate unstylish captions than apply style shift techn 阅读全文
posted @ 2019-06-26 23:14 一窍不通 阅读(1552) 评论(0) 推荐(0) 编辑
摘要: Text Style Transfer主要是指Non-Parallel Data条件下的,具体的paper list见: https://github.com/fuzhenxin/Style-Transfer-in-Text Delete, Retrieve, Generate: A Simple 阅读全文
posted @ 2019-06-26 23:13 一窍不通 阅读(1780) 评论(0) 推荐(0) 编辑
摘要: 这篇涉及到以下三篇论文: Unpaired Image Captioning by Language Pivoting (ECCV 2018) Show, Tell and Discriminate: Image Captioning by Self-retrieval with Partially 阅读全文
posted @ 2019-05-30 18:07 一窍不通 阅读(1043) 评论(0) 推荐(0) 编辑
摘要: Code from: https://github.com/SeitaroShinagawa/simple_beamsearch 阅读全文
posted @ 2019-03-26 19:37 一窍不通 阅读(294) 评论(0) 推荐(0) 编辑
摘要: 说明: 这个合辑里面的论文不全是Image Caption, 但大多和Image Caption相关, 同时还有一些Workshop论文。 Guiding Long-Short Term Memory for Image Caption Generation (ICCV 2015) Highligh 阅读全文
posted @ 2018-12-03 15:18 一窍不通 阅读(2337) 评论(0) 推荐(0) 编辑
摘要: Image caption generation: https://github.com/eladhoffer/captionGen Simple encoder-decoder image captioning: https://github.com/udacity/CVND Image-Capt 阅读全文
posted @ 2018-12-03 15:16 一窍不通 阅读(4666) 评论(0) 推荐(0) 编辑
摘要: A Hierarchical Approach for Generating Descriptive Image Paragraphs (CPVR 2017) Li Fei-Fei. 数据集地址: http://cs.stanford.edu/people/ranjaykrishna/im2p/in 阅读全文
posted @ 2018-12-03 14:43 一窍不通 阅读(918) 评论(0) 推荐(0) 编辑
摘要: 2017 Python最新面试题及答案16道题 15个重要Python面试题 测测你适不适合做Python? torch.squeeze() Returns a tensor with all the dimensions of input of size 1 removed. Python 3:f 阅读全文
posted @ 2018-10-30 19:06 一窍不通 阅读(1330) 评论(0) 推荐(0) 编辑
摘要: LSTM’s in Pytorch Example: An LSTM for Part-of-Speech Tagging Exercise: Augmenting the LSTM part-of-speech tagger with character-level features Sequen 阅读全文
posted @ 2018-10-24 16:41 一窍不通 阅读(353) 评论(0) 推荐(0) 编辑
摘要: Word Embeddings: Encoding Lexical Semantics Getting Dense Word Embeddings Word Embeddings in Pytorch An Example: N-Gram Language Modeling Exercise: Co 阅读全文
posted @ 2018-10-23 22:39 一窍不通 阅读(450) 评论(0) 推荐(0) 编辑
摘要: 原文地址: Generating Names with Character-Level RNN 搬运只为督促自己学习,没有其他目的。 Preparing the Data Download the data from here and extract it to the current direct 阅读全文
posted @ 2018-10-23 14:06 一窍不通 阅读(306) 评论(0) 推荐(0) 编辑
摘要: 原文地址:https://github.com/Kaixhin/grokking-pytorch PyTorch is a flexible deep learning framework that allows automatic differentiation(自动求导) through dyn 阅读全文
posted @ 2018-10-20 20:37 一窍不通 阅读(520) 评论(0) 推荐(0) 编辑