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020 _a9783031022555
_9978-3-031-02255-5
024 7 _a10.1007/978-3-031-02255-5
_2doi
050 4 _aT1-995
072 7 _aTBC
_2bicssc
072 7 _aTEC000000
_2bisacsh
072 7 _aTBC
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082 0 4 _a620
_223
100 1 _aNie, Liqiang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987141
245 1 0 _aMultimodal Learning toward Micro-Video Understanding
_h[electronic resource] /
_cby Liqiang Nie, Meng Liu, Xuemeng Song.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXV, 170 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Image, Video, and Multimedia Processing,
_x1559-8144
505 0 _aPreface -- Acknowledgments -- Introduction -- Data Collection -- Multimodal Transductive Learning for Micro-Video Popularity Prediction -- Multimodal Cooperative Learning for Micro-Video Venue Categorization -- Multimodal Transfer Learning in Micro-Video Analysis -- Multimodal Sequential Learning for Micro-Video Recommendation -- Research Frontiers -- Bibliography -- Authors' Biographies.
520 _aMicro-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.
650 0 _aEngineering.
_99405
650 0 _aElectrical engineering.
_987142
650 0 _aSignal processing.
_94052
650 1 4 _aTechnology and Engineering.
_987144
650 2 4 _aElectrical and Electronic Engineering.
_987145
650 2 4 _aSignal, Speech and Image Processing.
_931566
700 1 _aLiu, Meng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987148
700 1 _aSong, Xuemeng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987150
710 2 _aSpringerLink (Online service)
_987152
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031002168
776 0 8 _iPrinted edition:
_z9783031011276
776 0 8 _iPrinted edition:
_z9783031033834
830 0 _aSynthesis Lectures on Image, Video, and Multimedia Processing,
_x1559-8144
_987154
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02255-5
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