Computational Methods for Integrating Vision and Language (Record no. 84912)
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fixed length control field | 04116nam a22005295i 4500 |
001 - CONTROL NUMBER | |
control field | 978-3-031-01814-5 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20240730163723.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 220601s2016 sz | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
ISBN | 9783031018145 |
-- | 978-3-031-01814-5 |
082 04 - CLASSIFICATION NUMBER | |
Call Number | 006 |
100 1# - AUTHOR NAME | |
Author | Kanatani, Kenichi. |
245 10 - TITLE STATEMENT | |
Title | Computational Methods for Integrating Vision and Language |
250 ## - EDITION STATEMENT | |
Edition statement | 1st ed. 2016. |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | XVI, 211 p. |
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Series statement | Synthesis Lectures on Computer Vision, |
505 0# - FORMATTED CONTENTS NOTE | |
Remark 2 | Acknowledgments -- Figure Credits -- Introduction -- The Semantics of Images and Associated Text -- Sources of Data for Linking Visual and Linguistic Information -- Extracting and Representing Visual Information -- Text and Speech Processing -- Modeling Images and Keywords -- Beyond Simple Nouns -- Sequential Structure -- Bibliography -- Author's Biography. |
520 ## - SUMMARY, ETC. | |
Summary, etc | 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 semantics, going from words about the scene taken as whole, to words that apply to specific parts of the scene, to relationships between parts. Methods going beyond keywords are organized by the linguistic roles that are learned, exploited, or generated. These include proper nouns, adjectives, spatial and comparative prepositions, and verbs. More recent developments in dealing with sequential structure include automated captioning of scenes and video, alignment of video and text, and automated answering of questions about scenes depicted in images. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
General subdivision | Digital techniques. |
700 1# - AUTHOR 2 | |
Author 2 | Sugaya, Yasuyuki. |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-01814-5 |
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Koha item type | eBooks |
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-- | Springer International Publishing : |
-- | Imprint: Springer, |
-- | 2016. |
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-- | computer |
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-- | online resource |
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-- | text file |
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Image processing |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer vision. |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Pattern recognition systems. |
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer Imaging, Vision, Pattern Recognition and Graphics. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer Vision. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Automated Pattern Recognition. |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
-- | 2153-1064 |
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