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Multimodal Learning toward Micro-Video Understanding (Paperback)
Loot Price: R1,793
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Multimodal Learning toward Micro-Video Understanding (Paperback)
Series: Synthesis Lectures on Image, Video, and Multimedia Processing
Expected to ship within 10 - 15 working days
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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.
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