常见的image captioning系统是由一个CNN+RNN的编码解码模型完成,类比一下machine translation系统,通常由一个RNN encoder + RNN decoder组成: 而image captioning系统,通常由一个CNN encoder + RNN decoder组成: 其中的CNN能够提取一张图片的特征,其特征能用来做图片分类,目标识别,图片分割,及其他视觉任务。Vinyals et...
因此近几年来大量的工作致力于图像字幕（image captioning），这项任务简而言之就是“使用语法和语义正确...
论文:Attention on Attention for Image Captioning 链接:https://arxiv.org/abs/1908.06954 源码:https://github.com/husthuaan/AoANet 这篇文章主要是对注意力机制的改进,作者提出了“attention on attention”的方法,该方法通过计算注意力的结果与输入query的相关性来对信息进行过滤,作者最后将该方法运用在编码器和...
联系视觉和语言在通用人工智能中起着至关重要的作用。因此近几年来大量的工作致力于图像字幕(image captioning),这项任务简而言之就是“使用语法和语义正确的语言描述图像”。 从2015 年开始这项任务的 pipeline 就被分为了两部分,第一阶段即对图像特征进行编码,第二阶段生成语句。这两年来,随着对物体对象区域,属性...
Candidate:the the the the the the the.Reference1:Thecatison the mat.Reference2:Thereisa cat on the mat. 如果这种情况用precision来计算,candidate translation只有一种字就是the,而不管是reference1还是reference2只要一个中包含the,那么the就是预测正确的字,预测正确的字的个数为7,candidate translation总的...
Use the API Image captions Dense captions The image captioning feature is part of the Analyze Image API. Include Caption in the features query parameter. Then, when you get the full JSON response, parse the string for the contents of the "captionResult" section. Next steps Learn the related...
Image captioning is an interesting problem in the intersection between computer vision and natural language processing, and it has attracted great attention from their respective research communities. Recent image captioning models have achieved impressive results on the tasks where large am...
image caption笔记(九):《Unsupervised Image Captioning》 无监督的caption 文章使用一个图像数据集(MSCOCO)和一个文本语料库(从Web上抓取的200多万个句子组成图像描述语料库) 来做无监督caption。没有任何配对集合。 1、模型结构: 提出的图像字幕模型由图像编码器(没有用VGG 和resnet,改用了Inception v4),句子...
Although image captioning has made great progress in describing images, current methods are limited in 2D recognition of salient and moving objects. This leads to sentences that lack information about static and background objects, with poor performance on words' order and prepositions, which cannot ...
tokenizer.fit_on_texts(lines) dump(tokenizer,open('tokenizer.pkl','wb')) 2.3 生成输入数据结构 为了训练LSTM, 训练数据中的每⼀个图像的每⼀个标题都需要被重新拆分为输⼊和输出部分. 如果标题为”a cat sits on the table”, 需要添加起始和结束标志, 变为 ‘startseq a cat sits on the table...