典型文献
Visuals to Text:A Comprehensive Review on Automatic Image Captioning
文献摘要：
Image captioning refers to automatic generation of descriptive texts according to the visual content of images.It is a technique integrating multiple disciplines including the computer vision(CV),natural language processing(NLP)and artificial intelligence.In recent years,substantial research efforts have been devoted to generate image caption with impressive progress.To summarize the recent advances in image captioning,we present a comprehensive review on image captioning,covering both traditional methods and recent deep learning-based techniques.Specifically,we first briefly review the early traditional works based on the retrieval and template.Then deep learning-based image captioning researches are focused,which is categorized into the encoder-decoder framework,attention mechanism and training strategies on the basis of model structures and training manners for a detailed introduction.After that,we summarize the publicly available datasets,evaluation metrics and those proposed for specific requirements,and then compare the state of the art methods on the MS COCO dataset.Finally,we provide some discussions on open challenges and future research directions.
文献关键词：
中图分类号：
作者姓名：
Yue Ming;Nannan Hu;Chunxiao Fan;Fan Feng;Jiangwan Zhou;Hui Yu
作者机构：
Beijing University of Posts and Telecommunications,Beijing 100876,China;School of Creative Technologies,University of Ports-mouth,Portsmouth PO1 2DJ,UK
文献出处：
引用格式：
[1]Yue Ming;Nannan Hu;Chunxiao Fan;Fan Feng;Jiangwan Zhou;Hui Yu-.Visuals to Text:A Comprehensive Review on Automatic Image Captioning)[J].自动化学报（英文版）,2022(08):1339-1365
A类：
Visuals,caption
B类：
Text,Comprehensive,Review,Automatic,Image,Captioning,captioning,refers,automatic,generation,descriptive,texts,according,visual,content,images,It,integrating,multiple,disciplines,including,computer,vision,CV,natural,language,processing,NLP,artificial,intelligence,In,recent,years,substantial,efforts,have,been,devoted,generate,impressive,progress,To,summarize,advances,we,present,comprehensive,review,covering,both,traditional,methods,deep,learning,techniques,Specifically,first,briefly,early,works,retrieval,template,Then,researches,focused,which,categorized,into,encoder,decoder,framework,attention,mechanism,training,strategies,basis,model,structures,manners,detailed,introduction,After,that,publicly,available,datasets,evaluation,metrics,those,proposed,specific,requirements,then,compare,state,COCO,Finally,provide,some,discussions,open,challenges,future,directions
AB值：
0.668913
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