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008			230330s2019 sz | s |||| 0|eng d
020			_a9783031022555
024	7		_a10.1007/978-3-031-02255-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aHM742 _b2019 EB
100	1		_aNie, Liqiang _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686432
245	1	0	_aMultimodal Learning toward Micro-Video Understanding _cby Liqiang Nie, Meng Liu, Xuemeng Song
250			_a1st edition 2019
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XV, 170 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2019
650		7	_2embne _9158169 _aInteligencias múltiples
700	1		_aLiu, Meng _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687911
700	1		_aSong, Xuemeng _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686433
776	0	8	_iPrinted edition: _z9783031002168
776	0	8	_iPrinted edition: _z9783031011276
776	0	8	_iPrinted edition: _z9783031033834
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02255-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eIG _zSI