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Titlebook: Computational Methods for Integrating Vision and Language; Kobus Barnard Book 2016 Springer Nature Switzerland AG 2016

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書目名稱Computational Methods for Integrating Vision and Language
編輯Kobus Barnard
視頻videohttp://file.papertrans.cn/233/232723/232723.mp4
叢書名稱Synthesis Lectures on Computer Vision
圖書封面Titlebook: Computational Methods for Integrating Vision and Language;  Kobus Barnard Book 2016 Springer Nature Switzerland AG 2016
描述Modeling data from visual and linguistic modalities together creates opportunities for better understanding of both, and supports many useful applications. Examples of dual visual-linguistic data includes images with keywords, video with narrative, and figures in documents. We consider two key task-driven themes: translating from one modality to another (e.g., inferring annotations for images) and understanding the data using all modalities, where one modality can help disambiguate information in another. The multiple modalities can either be essentially semantically redundant (e.g., keywords provided by a person looking at the image), or largely complementary (e.g., meta data such as the camera used). Redundancy and complementarity are two endpoints of a scale, and we observe that good performance on translation requires some redundancy, and that joint inference is most useful where some information is complementary. Computational methods discussed are broadly organized into ones forsimple keywords, ones going beyond keywords toward natural language, and ones considering sequential aspects of natural language. Methods for keywords are further organized based on localization of sem
出版日期Book 2016
版次1
doihttps://doi.org/10.1007/978-3-031-01814-5
isbn_softcover978-3-031-00686-9
isbn_ebook978-3-031-01814-5Series ISSN 2153-1056 Series E-ISSN 2153-1064
issn_series 2153-1056
copyrightSpringer Nature Switzerland AG 2016
The information of publication is updating

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Sources of Data for Linking Visual and Linguistic Information,e I catalog many of the data sets that have been used. I begin with the WordNet text resource, which is commonly used to anchor text in datasets with respect to semantics, as well being used for preprocessing (Chapter 5) and joint learning. I then describe datasets that provide images or videos toge
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Extracting and Representing Visual Information, scope. For example, semantics can pertain to the entire scene (e.g., birthday, sunset, frightening), objects within (cars, people, dogs), parts of objects, backgrounds (e.g., sky, water), and even spatial relations between objects or backgrounds. Given appropriate localization, the appearance of ob
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Modeling Images and Keywords,lenging. The underlying goal.no less than jointly understanding vision and language.is vast, and progress reflects the need for researchers to focus on manageable sub-problems. Historically, one clear trend is increasingly sophisticated language modeling, which is our first organizing principle. Thi
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Computational Methods for Integrating Vision and Language978-3-031-01814-5Series ISSN 2153-1056 Series E-ISSN 2153-1064
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