Multimodal Learning toward Micro-Video Understanding (Synthesis Lectures on Image, Video, and Multimedia Processing) - Softcover
Synopsis
Micro-videos, a new form of user-generated contents, have been spreading widely across various social platforms, such as Vine, Kuaishou, and Tik Tok. Different from traditional long videos, micro-videos are usually recorded by smart mobile devices at any place within a few seconds. Due to its brevity and low bandwidth cost, micro-videos are gaining increasing user enthusiasm. The blossoming of micro-videos opens the door to the possibility of many promising applications, ranging from network content caching to online advertising. Thus, it is highly desirable to develop an effective scheme for the high-order micro-video understanding.
Micro-video understanding is, however, non-trivial due to the following challenges: (1) how to represent micro-videos that only convey one or few high-level themes or concepts; (2) how to utilize the hierarchical structure of the venue categories to guide the micro-video analysis; (3) how to alleviate the influence of low-quality caused by complex surrounding environments and the camera shake; (4) how to model the multimodal sequential data, {i.e.}, textual, acoustic, visual, and social modalities, to enhance the micro-video understanding; and (5) how to construct large-scale benchmark datasets for the analysis? These challenges have been largely unexplored to date. In this book, we focus on addressing the challenges presented above by proposing some state-of-the-art multimodal learning theories. To demonstrate the effectiveness of these models, we apply them to three practical tasks of micro-video understanding: popularity prediction, venue category estimation, and micro-video routing. Particularly, we first build three large-scale real-world micro-video datasets for these practical tasks. We then present a multimodal transductive learning framework for micro-video popularity prediction. Furthermore, we introduce several multimodal cooperative learning approaches and a multimodal transfer learning scheme for micro-video venue category estimation. Meanwhile, we develop a multimodal sequential learning approach for micro-video recommendation. Finally, we conclude the book and figure out the future research directions in multimodal learning toward micro-video understanding."synopsis" may belong to another edition of this title.
About the Author
Liqiang Nie is currently a professor with the School of Computer Science and Technology, Shandong University. In addition, he is the adjunct dean with the Shandong AI institute.H e received his B.Eng. and Ph.D. from Xian Jiaotong University in 2009 and the National University of Singapore (NUS) in 2013, respectively. After his Ph.D., Dr. Nie continued his research in NUS as a research fellow for three and half years. His research interests lie primarily in multimedia computing and information retrieval. Dr. Nie has authored and/or coauthored more than 100 papers for SIGIR, ACM MM, TOIS,and TIP, received more than 4,800 Google Scholar citations. He is an AE of Information Science, and an area chair of ACMMM 2018/2019.Meng Liu is currently a Ph.D. student with the School of Computing Science and Technology, Shandong University. She received an M.S. in computational mathematics from Dalian University of Technology, China in 2016. Her research interests are multimedia computing and information retrieval. Various parts of her work have been published in top forums and journals, such as SIGIR, MM, and IEEE TIP. She has served as reviewer and subreviewer for various conferences and journals, such as MMM, MM, PCM, JVCI, and INS.Xuemeng Song received a B.E. from the University of Science and Technology of China in 2012, and a Ph.D. from the School of Computing, National University of Singapore in 2016. She is currently an assistant professor of Shandong University, Jinan, China. Her research interests include information retrieval and social network analysis. She has published several papers in the top venues, such as ACM SIGIR, MM,and TOIS. In addition, she has served as a reviewer for many top conferences and journals.
"About this title" may belong to another edition of this title.
Other Popular Editions of the Same Title
Search results for Multimodal Learning toward Micro-Video Understanding...
Stock Image
Multimodal Learning toward Micro-Video Understanding (Synthesis Lectures on Image, Video, and Multimedia Processing)
Seller: Best Price, Torrance, CA, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. SUPER FAST SHIPPING. Seller Inventory # 9783031011276
Stock Image
Multimodal Learning toward Micro-Video Understanding (Synthesis Lectures on Image, Video, and Multimedia Processing)
Seller: Books Puddle, New York, NY, U.S.A.
Seller rating 4 out of 5 stars
Condition: New. 1st edition NO-PA16APR2015-KAP. Seller Inventory # 26394683623
Stock Image
Multimodal Learning toward Micro-Video Understanding (Synthesis Lectures on Image, Video, and Multimedia Processing)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In English. Seller Inventory # ria9783031011276_new
Buy New
US$ 80.22
Shipping:
US$ 15.78
From United Kingdom to U.S.A.
Quantity: Over 20 available
Stock Image
Multimodal Learning toward Micro-Video Understanding
Seller: Chiron Media, Wallingford, United Kingdom
Seller rating 4 out of 5 stars
PF. Condition: New. Seller Inventory # 6666-IUK-9783031011276
Buy New
US$ 78.85
Shipping:
US$ 20.40
From United Kingdom to U.S.A.
Quantity: 10 available
Seller Image
Multimodal Learning toward Micro-Video Understanding
Published by
Springer International Publishing Sep 2019, 2019
ISBN 10: 3031011279
ISBN 13: 9783031011276
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Micro-videos, a new form of user-generated contents, have been spreading widely across various social platforms, such as Vine, Kuaishou, and Tik Tok. Different from traditional long videos, micro-videos are usually recorded by smart mobile devices at any place within a few seconds. Due to its brevity and low bandwidth cost, micro-videos are gaining increasing user enthusiasm.The blossoming of micro-videos opens the door to the possibility of many promising applications, ranging from network content caching to online advertising. Thus, it is highly desirable to develop an effective scheme for the high-order micro-video understanding.Micro-video understanding is, however, non-trivial due to the following challenges:(1) how to represent micro-videos that only convey one or few high-level themes or concepts; (2) how to utilize the hierarchical structure of the venue categories to guide the micro-video analysis; (3) how to alleviate the influence of low-quality caused by complex surrounding environments and the camera shake; (4) how to model the multimodal sequential data, {i.e.}, textual, acoustic, visual, and social modalities, to enhance the micro-video understanding; and (5) how to construct large-scale benchmark datasets for the analysis These challenges have been largely unexplored to date.In this book, we focus on addressing the challenges presented above by proposing some state-of-the-art multimodal learning theories. To demonstrate the effectiveness of these models, we apply them to three practical tasks of micro-video understanding: popularity prediction, venue category estimation, and micro-video routing. Particularly, we first build three large-scale real-world micro-video datasets for these practical tasks. We then present a multimodal transductive learning framework for micro-video popularity prediction. Furthermore, we introduce several multimodal cooperative learning approaches and a multimodal transfer learning scheme for micro-video venue category estimation. Meanwhile, we develop a multimodal sequential learning approach for micro-video recommendation. Finally, we conclude the book and figure out the future research directions in multimodal learning toward micro-video understanding. 188 pp. Englisch. Seller Inventory # 9783031011276
Stock Image
Multimodal Learning toward Micro-Video Understanding (Synthesis Lectures on Image, Video, and Multimedia Processing)
Print on DemandSeller: Majestic Books, Hounslow, United Kingdom
Seller rating 4 out of 5 stars
Condition: New. Print on Demand. Seller Inventory # 401726264
Buy New
US$ 95.38
Shipping:
US$ 8.56
From United Kingdom to U.S.A.
Quantity: 4 available
Stock Image
Multimodal Learning toward Micro-Video Understanding (Synthesis Lectures on Image, Video, and Multimedia Processing)
Print on DemandSeller: Biblios, Frankfurt am main, HESSE, Germany
Seller rating 4 out of 5 stars
Condition: New. PRINT ON DEMAND. Seller Inventory # 18394683629
Seller Image
Multimodal Learning toward Micro-Video Understanding
ISBN 10: 3031011279
ISBN 13: 9783031011276
New
Softcover
Seller: moluna, Greven, Germany
Seller rating 4 out of 5 stars
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Micro-videos, a new form of user-generated contents, have been spreading widely across various social platforms, such as Vine, Kuaishou, and Tik Tok. Different from traditional long videos, micro-videos are usually recorded by smart mobile devices at any pl. Seller Inventory # 608129371
Buy New
US$ 66.39
Shipping:
US$ 56.62
From Germany to U.S.A.
Quantity: Over 20 available
Seller Image
Multimodal Learning toward Micro-Video Understanding
ISBN 10: 3031011279
ISBN 13: 9783031011276
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware -Micro-videos, a new form of user-generated contents, have been spreading widely across various social platforms, such as Vine, Kuaishou, and Tik Tok. Different from traditional long videos, micro-videos are usually recorded by smart mobile devices at any place within a few seconds. Due to its brevity and low bandwidth cost, micro-videos are gaining increasing user enthusiasm.The blossoming of micro-videos opens the door to the possibility of many promising applications, ranging from network content caching to online advertising. Thus, it is highly desirable to develop an effective scheme for the high-order micro-video understanding.Micro-video understanding is, however, non-trivial due to the following challenges:(1) how to represent micro-videos that only convey one or few high-level themes or concepts; (2) how to utilize the hierarchical structure of the venue categories to guide the micro-video analysis; (3) how to alleviate the influence of low-quality caused by complex surrounding environments and the camera shake; (4) how to model the multimodal sequential data, {i.e.}, textual, acoustic, visual, and social modalities, to enhance the micro-video understanding; and (5) how to construct large-scale benchmark datasets for the analysis These challenges have been largely unexplored to date.In this book, we focus on addressing the challenges presented above by proposing some state-of-the-art multimodal learning theories. To demonstrate the effectiveness of these models, we apply them to three practical tasks of micro-video understanding: popularity prediction, venue category estimation, and micro-video routing. Particularly, we first build three large-scale real-world micro-video datasets for these practical tasks. We then present a multimodal transductive learning framework for micro-video popularity prediction. Furthermore, we introduce several multimodal cooperative learning approaches and a multimodal transfer learning scheme for micro-video venue category estimation. Meanwhile, we develop a multimodal sequential learning approach for micro-video recommendation. Finally, we conclude the book and figure out the future research directions in multimodal learning toward micro-video understanding.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 188 pp. Englisch. Seller Inventory # 9783031011276
Seller Image
Multimodal Learning toward Micro-Video Understanding
Published by
Springer International Publishing, 2019
ISBN 10: 3031011279
ISBN 13: 9783031011276
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Micro-videos, a new form of user-generated contents, have been spreading widely across various social platforms, such as Vine, Kuaishou, and Tik Tok. Different from traditional long videos, micro-videos are usually recorded by smart mobile devices at any place within a few seconds. Due to its brevity and low bandwidth cost, micro-videos are gaining increasing user enthusiasm.The blossoming of micro-videos opens the door to the possibility of many promising applications, ranging from network content caching to online advertising. Thus, it is highly desirable to develop an effective scheme for the high-order micro-video understanding.Micro-video understanding is, however, non-trivial due to the following challenges:(1) how to represent micro-videos that only convey one or few high-level themes or concepts; (2) how to utilize the hierarchical structure of the venue categories to guide the micro-video analysis; (3) how to alleviate the influence of low-quality caused by complex surrounding environments and the camera shake; (4) how to model the multimodal sequential data, {i.e.}, textual, acoustic, visual, and social modalities, to enhance the micro-video understanding; and (5) how to construct large-scale benchmark datasets for the analysis These challenges have been largely unexplored to date.In this book, we focus on addressing the challenges presented above by proposing some state-of-the-art multimodal learning theories. To demonstrate the effectiveness of these models, we apply them to three practical tasks of micro-video understanding: popularity prediction, venue category estimation, and micro-video routing. Particularly, we first build three large-scale real-world micro-video datasets for these practical tasks. We then present a multimodal transductive learning framework for micro-video popularity prediction. Furthermore, we introduce several multimodal cooperative learning approaches and a multimodal transfer learning scheme for micro-video venue category estimation. Meanwhile, we develop a multimodal sequential learning approach for micro-video recommendation. Finally, we conclude the book and figure out the future research directions in multimodal learning toward micro-video understanding. Seller Inventory # 9783031011276