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Title: GitHub - 17MachineLearning/VideoCaption: 视频的文本摘要(标注)，输入一段视频，通过深度学习网络和人工智能程序识别视频主要表达的意思(Input a video output a txt decribing the video)。 · GitHub

Open Graph Title: GitHub - 17MachineLearning/VideoCaption: 视频的文本摘要(标注)，输入一段视频，通过深度学习网络和人工智能程序识别视频主要表达的意思(Input a video output a txt decribing the video)。

X Title: GitHub - 17MachineLearning/VideoCaption: 视频的文本摘要(标注)，输入一段视频，通过深度学习网络和人工智能程序识别视频主要表达的意思(Input a video output a txt decribing the video)。

Description: 视频的文本摘要(标注)，输入一段视频，通过深度学习网络和人工智能程序识别视频主要表达的意思(Input a video output a txt decribing the video)。 - 17MachineLearning/VideoCaption

Open Graph Description: 视频的文本摘要(标注)，输入一段视频，通过深度学习网络和人工智能程序识别视频主要表达的意思(Input a video output a txt decribing the video)。 - 17MachineLearning/VideoCaption

X Description: 视频的文本摘要(标注)，输入一段视频，通过深度学习网络和人工智能程序识别视频主要表达的意思(Input a video output a txt decribing the video)。 - 17MachineLearning/VideoCaption

Opengraph URL: https://github.com/17MachineLearning/VideoCaption

X: @github

direct link

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og:image:alt	视频的文本摘要(标注)，输入一段视频，通过深度学习网络和人工智能程序识别视频主要表达的意思(Input a video output a txt decribing the video)。 - 17MachineLearning/VideoCaption
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README	https://github.com/17MachineLearning/VideoCaption
	https://github.com/17MachineLearning/VideoCaption#videocaption
	https://github.com/17MachineLearning/VideoCaption#本程序总共包含3个模块
	https://github.com/17MachineLearning/VideoCaption#依赖包
模型文件链接	https://pan.baidu.com/s/1thMC-pQ31xgl68ceDuIERg
	https://github.com/17MachineLearning/VideoCaption#python3-numpy-opencv-pytorch-jieba分词-textrank4zh-tdqm-opencc-gensim-预训练的模型文件image-caption模块的模型文件和text-summary模型文件链接模型文件链接使用方法
	https://github.com/CaptainEven/VideoCaption/blob/master/screen%20shots/result.png
	https://github.com/17MachineLearning/VideoCaption#脚本运行效果截图测试的视频是薛之谦的演员mv--算法主要步骤和脚本文件详解
	https://github.com/17MachineLearning/VideoCaption#1short-detector模块
	https://github.com/17MachineLearning/VideoCaption#2-image-caption模块
训练数据来源-全球AI挑战赛-图像中文描述	https://challenger.ai/competition/caption/
	https://github.com/17MachineLearning/VideoCaption#1-这里使用resnet作为图像高层次语义特征提取模型不能直接使用resnet模型需要做出一点修改
	https://github.com/17MachineLearning/VideoCaption#3-文本摘要模块text-summary
	https://github.com/17MachineLearning/VideoCaption#1-将句子转化为词频向量然后计算词频向量之间的夹角余弦作为句子之间的相似度即textrank4zh的做法
	https://github.com/17MachineLearning/VideoCaption#2-词向量是多维实数向量向量中包含了自然语言中的语义和语法关系信息不仅考虑了上下文信息而且减少了冗余信息因此可以很自然可以想到利用word2vect计算句子与句子之间的相似性
	https://github.com/17MachineLearning/VideoCaption#3-文本摘要还可以其他深度学习的方式比如通过训练一个seq2seq模型来完成这部分内容还在测试后续会补上
	https://github.com/17MachineLearning/VideoCaption#关于文本摘要使用的维基百科中文预料处理
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	https://github.com/17MachineLearning/VideoCaption#1-繁体转简体-
	https://github.com/17MachineLearning/VideoCaption#2-去除多余的符号清洗文本使用preprocess下的filter_wikipy脚本
	https://github.com/17MachineLearning/VideoCaption#3-使用preprocess下的cut2wordspy处理主要是通过jieba做分词处理
	https://github.com/17MachineLearning/VideoCaption#4-安装gensim通过train_word2vectpy训练word2vect模型训练结束得到3个模型文件利用gensim可以计算中文词语的词向量-
	https://github.com/CaptainEven/VideoCaption/blob/master/screen%20shots/text_sum.png
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