000 -LEADER |
fixed length control field |
06619nam a2200769 i 4500 |
001 - CONTROL NUMBER |
control field |
8845048 |
003 - CONTROL NUMBER IDENTIFIER |
control field |
IEEE |
005 - DATE AND TIME OF LATEST TRANSACTION |
control field |
20200413152933.0 |
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
fixed length control field |
m eo d |
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
fixed length control field |
cr cn |||m|||a |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
fixed length control field |
190927s2019 caua fob 000 0 eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
International Standard Book Number |
9781681736297 |
Qualifying information |
electronic |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
Canceled/invalid ISBN |
9781681736303 |
Qualifying information |
hardcover |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
Canceled/invalid ISBN |
9781681736280 |
Qualifying information |
paperback |
024 7# - OTHER STANDARD IDENTIFIER |
Standard number or code |
10.2200/S00938ED1V01Y201907IVM020 |
Source of number or code |
doi |
035 ## - SYSTEM CONTROL NUMBER |
System control number |
(CaBNVSL)thg00979531 |
035 ## - SYSTEM CONTROL NUMBER |
System control number |
(OCoLC)1121141680 |
040 ## - CATALOGING SOURCE |
Original cataloging agency |
CaBNVSL |
Language of cataloging |
eng |
Description conventions |
rda |
Transcribing agency |
CaBNVSL |
Modifying agency |
CaBNVSL |
050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
Classification number |
HM742 |
Item number |
.N546 2019eb |
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
Classification number |
302.23/1 |
Edition number |
23 |
100 1# - MAIN ENTRY--PERSONAL NAME |
Personal name |
Nie, Liqiang, |
Relator term |
author. |
245 10 - TITLE STATEMENT |
Title |
Multimodal learning toward micro-video understanding / |
Statement of responsibility, etc. |
Liqiang Nie, Meng Liu, and Xuemeng Song. |
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
Place of production, publication, distribution, manufacture |
[San Rafael, California] : |
Name of producer, publisher, distributor, manufacturer |
Morgan & Claypool, |
Date of production, publication, distribution, manufacture, or copyright notice |
[2019] |
300 ## - PHYSICAL DESCRIPTION |
Extent |
1 PDF (xv, 170 pages) : |
Other physical details |
color illustrations. |
336 ## - CONTENT TYPE |
Content type term |
text |
Source |
rdacontent |
337 ## - MEDIA TYPE |
Media type term |
electronic |
Source |
isbdmedia |
338 ## - CARRIER TYPE |
Carrier type term |
online resource |
Source |
rdacarrier |
490 1# - SERIES STATEMENT |
Series statement |
Synthesis lectures on image, video, and multimedia processing, |
International Standard Serial Number |
1559-8144 ; |
Volume/sequential designation |
#20 |
538 ## - SYSTEM DETAILS NOTE |
System details note |
Mode of access: World Wide Web. |
538 ## - SYSTEM DETAILS NOTE |
System details note |
System requirements: Adobe Acrobat Reader. |
500 ## - GENERAL NOTE |
General note |
Part of: Synthesis digital library of engineering and computer science. |
504 ## - BIBLIOGRAPHY, ETC. NOTE |
Bibliography, etc. note |
Includes bibliographical references (pages). |
505 0# - FORMATTED CONTENTS NOTE |
Formatted contents note |
1. Introduction -- 1.1. Micro-video proliferation -- 1.2. Practical tasks -- 1.3. Research challenges -- 1.4. Our solutions -- 1.5. Book structure |
505 8# - FORMATTED CONTENTS NOTE |
Formatted contents note |
2. Data collection -- 2.1. Dataset i for popularity prediction -- 2.2. Dataset ii for venue category estimation -- 2.3. Dataset iii for micro-video routing -- 2.4. Summary |
505 8# - FORMATTED CONTENTS NOTE |
Formatted contents note |
3. Multimodal transductive learning for micro-video popularity prediction -- 3.1. Background -- 3.2. Research problems -- 3.3. Feature extraction -- 3.4. Related work -- 3.5. Notations and preliminaries -- 3.6. Multimodal transductive learning -- 3.7. Multi-modal transductive low-rank learning -- 3.8. Summary |
505 8# - FORMATTED CONTENTS NOTE |
Formatted contents note |
4. Multimodal cooperative learning for micro-video venue categorization -- 4.1. Background -- 4.2. Research problems -- 4.3. Related work -- 4.4. Multimodal consistent learning -- 4.5. Multimodal complementary learning -- 4.6. Multimodal cooperative learning -- 4.7. Summary |
505 8# - FORMATTED CONTENTS NOTE |
Formatted contents note |
5. Multimodal transfer learning in micro-video analysis -- 5.1. Background -- 5.2. Research problems -- 5.3. Related work -- 5.4. External sound dataset -- 5.5. Deep multi-modal transfer learning -- 5.6. Experiments -- 5.7. Summary |
505 8# - FORMATTED CONTENTS NOTE |
Formatted contents note |
6. Multimodal sequential learning for micro-video recommendation -- 6.1. Background -- 6.2. Research problems -- 6.3. Related work -- 6.4. Multimodal sequential learning -- 6.5. Experiments -- 6.6. Summary |
505 8# - FORMATTED CONTENTS NOTE |
Formatted contents note |
7. Research frontiers -- 7.1. Micro-video annotation -- 7.2. Micro-video captioning -- 7.3. Micro-video thumbnail selection -- 7.4. Semantic ontology construction -- 7.5. Pornographic content identification. |
506 ## - RESTRICTIONS ON ACCESS NOTE |
Terms governing access |
Abstract freely available; full-text restricted to subscribers or individual document purchasers. |
510 0# - CITATION/REFERENCES NOTE |
Name of source |
Compendex |
510 0# - CITATION/REFERENCES NOTE |
Name of source |
INSPEC |
510 0# - CITATION/REFERENCES NOTE |
Name of source |
Google scholar |
510 0# - CITATION/REFERENCES NOTE |
Name of source |
Google book search |
520 ## - SUMMARY, ETC. |
Summary, etc. |
Micro-videos, a new form of user-generated content, have been spreading widely across various social platforms, such as Vine, Kuaishou, and TikTok. Different from traditional long videos, micro-videos are usually recorded by smart mobile devices at any place within a few seconds. Due to their 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 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 venue categories to guide micro-video analysis; (3) how to alleviate the influence of low quality caused by complex surrounding environments and camera shake; (4) how to model multimodal sequential data, i.e. textual, acoustic, visual, and social modalities to enhance micro-video understanding; and (5) how to construct large-scale benchmark datasets for 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. |
530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
Additional physical form available note |
Also available in print. |
588 ## - SOURCE OF DESCRIPTION NOTE |
Source of description note |
Title from PDF title page (viewed on September 27, 2019). |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name entry element |
Social media |
General subdivision |
Data processing. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name entry element |
Social media |
General subdivision |
Forecasting. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name entry element |
Learning. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name entry element |
Multiple intelligences. |
653 ## - INDEX TERM--UNCONTROLLED |
Uncontrolled term |
micro-video understanding |
653 ## - INDEX TERM--UNCONTROLLED |
Uncontrolled term |
multimodal transductive learning |
653 ## - INDEX TERM--UNCONTROLLED |
Uncontrolled term |
multimodal cooperative learning |
653 ## - INDEX TERM--UNCONTROLLED |
Uncontrolled term |
multimodal transfer learning |
653 ## - INDEX TERM--UNCONTROLLED |
Uncontrolled term |
multimodal sequential learning |
653 ## - INDEX TERM--UNCONTROLLED |
Uncontrolled term |
popularity prediction |
653 ## - INDEX TERM--UNCONTROLLED |
Uncontrolled term |
venue category estimation |
653 ## - INDEX TERM--UNCONTROLLED |
Uncontrolled term |
micro-video recommendation |
700 1# - ADDED ENTRY--PERSONAL NAME |
Personal name |
Liu, Meng |
Fuller form of name |
(Computer scientist), |
Relator term |
author. |
700 1# - ADDED ENTRY--PERSONAL NAME |
Personal name |
Song, Xuemeng |
Titles and other words associated with a name |
(Computer scientist), |
Relator term |
author. |
776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
Relationship information |
Print version: |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
Uniform title |
Synthesis lectures on image, video, and multimedia processing ; |
Volume/sequential designation |
#20. |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
Uniform title |
Synthesis digital library of engineering and computer science. |
856 40 - ELECTRONIC LOCATION AND ACCESS |
Materials specified |
Abstract with links to full text |
Uniform Resource Identifier |
https://doi.org/10.2200/S00938ED1V01Y201907IVM020 |
856 42 - ELECTRONIC LOCATION AND ACCESS |
Materials specified |
Abstract with links to resource |
Uniform Resource Identifier |
https://ieeexplore.ieee.org/servlet/opac?bknumber=8845048 |