The Resource Multimodal learning toward micro-video understanding, Liqiang Nie, Meng Liu, and Xuemeng Song
Multimodal learning toward micro-video understanding, Liqiang Nie, Meng Liu, and Xuemeng Song
Resource Information
The item Multimodal learning toward micro-video understanding, Liqiang Nie, Meng Liu, and Xuemeng Song represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.This item is available to borrow from 1 library branch.
Resource Information
The item Multimodal learning toward micro-video understanding, Liqiang Nie, Meng Liu, and Xuemeng Song represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.
This item is available to borrow from 1 library branch.
- Summary
- 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
- Language
- eng
- Extent
- 1 PDF (xv, 170 pages)
- Note
- Part of: Synthesis digital library of engineering and computer science
- Contents
-
- 1. Introduction -- 1.1. Micro-video proliferation -- 1.2. Practical tasks -- 1.3. Research challenges -- 1.4. Our solutions -- 1.5. Book structure
- 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
- 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
- 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
- 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
- 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
- 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
- Isbn
- 9781681736297
- Label
- Multimodal learning toward micro-video understanding
- Title
- Multimodal learning toward micro-video understanding
- Statement of responsibility
- Liqiang Nie, Meng Liu, and Xuemeng Song
- Language
- eng
- Summary
- 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
- Cataloging source
- CaBNVSL
- http://library.link/vocab/creatorName
- Nie, Liqiang
- Dewey number
- 302.23/1
- Illustrations
- illustrations
- Index
- no index present
- LC call number
- HM742
- LC item number
- .N546 2019eb
- Literary form
- non fiction
- Nature of contents
-
- dictionaries
- bibliography
- http://library.link/vocab/relatedWorkOrContributorName
-
- Liu, Meng
- Song, Xuemeng
- Series statement
- Synthesis lectures on image, video, and multimedia processing,
- Series volume
- 20
- http://library.link/vocab/subjectName
-
- Social media
- Social media
- Learning
- Multiple intelligences
- Target audience
-
- adult
- specialized
- Label
- Multimodal learning toward micro-video understanding, Liqiang Nie, Meng Liu, and Xuemeng Song
- Note
- Part of: Synthesis digital library of engineering and computer science
- Bibliography note
- Includes bibliographical references (pages)
- Carrier category
- online resource
- Carrier MARC source
- rdacarrier
- Color
- multicolored
- Content category
- text
- Content type MARC source
- rdacontent
- Contents
-
- 1. Introduction -- 1.1. Micro-video proliferation -- 1.2. Practical tasks -- 1.3. Research challenges -- 1.4. Our solutions -- 1.5. Book structure
- 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
- 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
- 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
- 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
- 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
- 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
- Control code
- 201907IVM020
- Dimensions
- unknown
- Extent
- 1 PDF (xv, 170 pages)
- File format
- multiple file formats
- Form of item
- online
- Isbn
- 9781681736297
- Media category
- electronic
- Media MARC source
- isbdmedia
- Other physical details
- color illustrations.
- Reformatting quality
- access
- Specific material designation
- remote
- System control number
-
- (CaBNVSL)thg00979531
- (OCoLC)1121141680
- Label
- Multimodal learning toward micro-video understanding, Liqiang Nie, Meng Liu, and Xuemeng Song
- Note
- Part of: Synthesis digital library of engineering and computer science
- Bibliography note
- Includes bibliographical references (pages)
- Carrier category
- online resource
- Carrier MARC source
- rdacarrier
- Color
- multicolored
- Content category
- text
- Content type MARC source
- rdacontent
- Contents
-
- 1. Introduction -- 1.1. Micro-video proliferation -- 1.2. Practical tasks -- 1.3. Research challenges -- 1.4. Our solutions -- 1.5. Book structure
- 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
- 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
- 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
- 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
- 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
- 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
- Control code
- 201907IVM020
- Dimensions
- unknown
- Extent
- 1 PDF (xv, 170 pages)
- File format
- multiple file formats
- Form of item
- online
- Isbn
- 9781681736297
- Media category
- electronic
- Media MARC source
- isbdmedia
- Other physical details
- color illustrations.
- Reformatting quality
- access
- Specific material designation
- remote
- System control number
-
- (CaBNVSL)thg00979531
- (OCoLC)1121141680
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.liverpool.ac.uk/portal/Multimodal-learning-toward-micro-video/4xr1ZDFnoJQ/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.liverpool.ac.uk/portal/Multimodal-learning-toward-micro-video/4xr1ZDFnoJQ/">Multimodal learning toward micro-video understanding, Liqiang Nie, Meng Liu, and Xuemeng Song</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.liverpool.ac.uk/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.liverpool.ac.uk/">Sydney Jones Library, University of Liverpool</a></span></span></span></span></div>
