Liqiang Nie's Books

Graph Learning for Fashion Compatibility Modeling

By: Weili Guan,Xuemeng Song,Xiaojun Chang,Liqiang Nie

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Graph Learning for Fashion Compatibility Modeling

By: Weili Guan,Xuemeng Song,Xiaojun Chang,Liqiang Nie

This book sheds light on state-of-the-art theories for more challenging outfit compatibility modeling scenarios. In particular, this book presents several cutting-edge graph learning techniques that can be used for outfit compatibility modeling. Due to its remarkable economic value, fashion compatibility modeling has gained increasing research attention in recent years. Although great efforts have been dedicated to this research area, previous studies mainly focused on fashion compatibility modeling for outfits that only involved two items and overlooked the fact that each outfit may be composed of a variable number of items. This book develops a series of graph-learning based outfit compatibility modeling schemes, all of which have been proven to be effective over several public real-world datasets. This systematic approach benefits readers by introducing the techniques for compatibility modeling of outfits that involve a variable number of composing items. To deal with the challenging task of outfit compatibility modeling, this book provides comprehensive solutions, including correlation-oriented graph learning, modality-oriented graph learning, unsupervised disentangled graph learning, partially supervised disentangled graph learning, and metapath-guided heterogeneous graph learning. Moreover, this book sheds light on research frontiers that can inspire future research directions for scientists and researchers.

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Page Count

120

Publisher

Springer Nature

Published Date

ISBN 10

3031188179

ISBN 13

9783031188176

Multimodal Learning toward Micro-Video Understanding

By: Liqiang Nie,Meng Liu,Xuemeng Song

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Multimodal Learning toward Micro-Video Understanding

By: Liqiang Nie,Meng Liu,Xuemeng Song

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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Page Count

170

Publisher

Springer Nature

Published Date

ISBN 10

3031022556

ISBN 13

9783031022555

Compatibility Modeling

Data and Knowledge Applications for Clothing Matching

By: Xuemeng Song,Liqiang Nie,Yinglong Wang

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Compatibility Modeling

Data and Knowledge Applications for Clothing Matching

By: Xuemeng Song,Liqiang Nie,Yinglong Wang

Nowadays, fashion has become an essential aspect of people's daily life. As each outfit usually comprises several complementary items, such as a top, bottom, shoes, and accessories, a proper outfit largely relies on the harmonious matching of these items. Nevertheless, not everyone is good at outfit composition, especially those who have a poor fashion aesthetic. Fortunately, in recent years the number of online fashion-oriented communities, like IQON and Chictopia, as well as e-commerce sites, like Amazon and eBay, has grown. The tremendous amount of real-world data regarding people's various fashion behaviors has opened a door to automatic clothing matching. Despite its significant value, compatibility modeling for clothing matching that assesses the compatibility score for a given set of (equal or more than two) fashion items, e.g., a blouse and a skirt, yields tough challenges: (a) the absence of comprehensive benchmark; (b) comprehensive compatibility modeling with the multi-modal feature variables is largely untapped; (c) how to utilize the domain knowledge to guide the machine learning; (d) how to enhance the interpretability of the compatibility modeling; and (e) how to model the user factor in the personalized compatibility modeling. These challenges have been largely unexplored to date. In this book, we shed light on several state-of-the-art theories on compatibility modeling. In particular, to facilitate the research, we first build three large-scale benchmark datasets from different online fashion websites, including IQON and Amazon. We then introduce a general data-driven compatibility modeling scheme based on advanced neural networks. To make use of the abundant fashion domain knowledge, i.e., clothing matching rules, we next present a novel knowledge-guided compatibility modeling framework. Thereafter, to enhance the model interpretability, we put forward a prototype-wise interpretable compatibility modeling approach. Following that, noticing the subjective aesthetics of users, we extend the general compatibility modeling to the personalized version. Moreover, we further study the real-world problem of personalized capsule wardrobe creation, aiming to generate a minimum collection of garments that is both compatible and suitable for the user. Finally, we conclude the book and present future research directions, such as the generative compatibility modeling, virtual try-on with arbitrary poses, and clothing generation.

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Page Count

118

Publisher

Springer Nature

Published Date

ISBN 10

3031023218

ISBN 13

9783031023217

Learning from Multiple Social Networks

By: Liqiang Nie,Xuemeng Song,Tat-Seng Chua

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Learning from Multiple Social Networks

By: Liqiang Nie,Xuemeng Song,Tat-Seng Chua

With the proliferation of social network services, more and more social users, such as individuals and organizations, are simultaneously involved in multiple social networks for various purposes. In fact, multiple social networks characterize the same social users from different perspectives, and their contexts are usually consistent or complementary rather than independent. Hence, as compared to using information from a single social network, appropriate aggregation of multiple social networks offers us a better way to comprehensively understand the given social users. Learning across multiple social networks brings opportunities to new services and applications as well as new insights on user online behaviors, yet it raises tough challenges: (1) How can we map different social network accounts to the same social users? (2) How can we complete the item-wise and block-wise missing data? (3) How can we leverage the relatedness among sources to strengthen the learning performance? And (4) How can we jointly model the dual-heterogeneities: multiple tasks exist for the given application and each task has various features from multiple sources? These questions have been largely unexplored to date. We noticed this timely opportunity, and in this book we present some state-of-the-art theories and novel practical applications on aggregation of multiple social networks. In particular, we first introduce multi-source dataset construction. We then introduce how to effectively and efficiently complete the item-wise and block-wise missing data, which are caused by the inactive social users in some social networks. We next detail the proposed multi-source mono-task learning model and its application in volunteerism tendency prediction. As a counterpart, we also present a mono-source multi-task learning model and apply it to user interest inference. We seamlessly unify these models with the so-called multi-source multi-task learning, and demonstrate several application scenarios, such as occupation prediction. Finally, we conclude the book and figure out the future research directions in multiple social network learning, including the privacy issues and source complementarity modeling. This is preliminary research on learning from multiple social networks, and we hope it can inspire more active researchers to work on this exciting area. If we have seen further it is by standing on the shoulders of giants.

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Page Count

102

Publisher

Springer Nature

Published Date

ISBN 10

3031023005

ISBN 13

9783031023002

Impact of Climate Change and Human Activity on the Eco-environment

An Analysis of the Xisha Islands

By: Liqiang Xu

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Impact of Climate Change and Human Activity on the Eco-environment

An Analysis of the Xisha Islands

By: Liqiang Xu

This study describes the fundamentals of assessing the vulnerability of coral islands, as well as environmental management and resource exploitation. Using seabird subfossils, such as bones, guano, eggshells etc., which have been well preserved on the Xisha Islands in the South China Sea, the author identifies the influences of climate change and human activity on seabird populations and diets. Understanding the past is of great importance for predicting the future, and seabird subfossils provide valuable information, which can be used to study changes in seabird ecology, paleoceanography and palaeoclimate. Furthermore, this study proposes examining the biogeochemical cycling of some elements present in the geosphere, hydrosphere, biosphere and atmosphere. Dr. Liqiang Xu works at the Hefei University of Technology, China.

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Page Count

161

Publisher

Springer

Published Date

ISBN 10

3662450038

ISBN 13

9783662450031

Vanadium-Based Nanomaterials for Electrochemical Energy Storage

By: Liqiang Mai,Lin Xu,Wei Chen

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Vanadium-Based Nanomaterials for Electrochemical Energy Storage

By: Liqiang Mai,Lin Xu,Wei Chen

This book presents a comprehensive review of recent developments in vanadium-based nanomaterials for next-generation electrochemical energy storage. The basic electrochemical energy storage and conversion equipment are elaborated, and the vanadium-based nanomaterials of the synthesis approaches, characterizations, electrochemical storage mechanisms, and performance optimization tactics are discussed. Examples are taken from various chemical energy storage devices to expound the functions of advanced vanadium-based nanomaterials for specific applications. Finally, various challenges and perspectives on vanadium-based nanomaterial development as an emerging energy storage solution are considered.

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Page Count

320

Publisher

Springer Nature

Published Date

ISBN 10

3031447964

ISBN 13

9783031447969

Multimodal Learning Toward Micro-Video Understanding

By: Liqiang Nie,Meng Liu,Xuemeng Song

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Multimodal Learning Toward Micro-Video Understanding

By: Liqiang Nie,Meng Liu,Xuemeng Song

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.

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Page Count

186

Publisher

Synthesis Lectures on Image, V

Published Date

ISBN 10

1681736306

ISBN 13

9781681736303

Learning from Multiple Social Networks

By: Liqiang Nie,Xuemeng Song,Tat-Seng Chua

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Learning from Multiple Social Networks

By: Liqiang Nie,Xuemeng Song,Tat-Seng Chua

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Page Count

120

Publisher

Morgan & Claypool Publishers

Published Date

ISBN 10

1627059865

ISBN 13

9781627059862

Graph Learning for Fashion Compatibility Modeling

By: Weili Guan,Xuemeng Song,Xiaojun Chang,Liqiang Nie

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Graph Learning for Fashion Compatibility Modeling

By: Weili Guan,Xuemeng Song,Xiaojun Chang,Liqiang Nie

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Page Count

120

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Springer Nature

Published Date

ISBN 10

3031188179

ISBN 13

9783031188176

Compatibility Modeling

Data and Knowledge Applications for Clothing Matching

By: Xuemeng Song,Liqiang Nie,Yinglong Wang

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Compatibility Modeling

Data and Knowledge Applications for Clothing Matching

By: Xuemeng Song,Liqiang Nie,Yinglong Wang

Nowadays, fashion has become an essential aspect of people's daily life. As each outfit usually comprises several complementary items, such as a top, bottom, shoes, and accessories, a proper outfit largely relies on the harmonious matching of these items. Nevertheless, not everyone is good at outfit composition, especially those who have a poor fashion aesthetic. Fortunately, in recent years the number of online fashion-oriented communities, like IQON and Chictopia, as well as e-commerce sites, like Amazon and eBay, has grown. The tremendous amount of real-world data regarding people's various fashion behaviors has opened a door to automatic clothing matching. Despite its significant value, compatibility modeling for clothing matching that assesses the compatibility score for a given set of (equal or more than two) fashion items, e.g., a blouse and a skirt, yields tough challenges: (a) the absence of comprehensive benchmark; (b) comprehensive compatibility modeling with the multi-modal feature variables is largely untapped; (c) how to utilize the domain knowledge to guide the machine learning; (d) how to enhance the interpretability of the compatibility modeling; and (e) how to model the user factor in the personalized compatibility modeling. These challenges have been largely unexplored to date. In this book, we shed light on several state-of-the-art theories on compatibility modeling. In particular, to facilitate the research, we first build three large-scale benchmark datasets from different online fashion websites, including IQON and Amazon. We then introduce a general data-driven compatibility modeling scheme based on advanced neural networks. To make use of the abundant fashion domain knowledge, i.e., clothing matching rules, we next present a novel knowledge guided compatibility modeling framework. Thereafter, to enhance the model interpretability, we put forward a prototype wise interpretable compatibility modeling approach. Following that, noticing the subjective aesthetics of users, we extend the general compatibility modeling to the personalized version. Moreover, we further study the real-world problem of personalized capsule wardrobe creation, aiming to generate a minimum collection of garments that is both compatible and suitable for the user. Finally, we conclude the book and present future research directions, such as the generative compatibility modeling, virtual try-on with arbitrary poses, and clothing generation.

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Age

Page Count

140

Publisher

Morgan & Claypool Publishers

Published Date

ISBN 10

1681736691

ISBN 13

9781681736693

Book's by Liqiang Nie

Graph Learning for Fashion Compatibility Modeling

Graph Learning for Fashion Compatibility Modeling

Multimodal Learning toward Micro-Video Understanding

Multimodal Learning toward Micro-Video Understanding

Compatibility Modeling

Compatibility Modeling

Learning from Multiple Social Networks

Learning from Multiple Social Networks

Impact of Climate Change and Human Activity on the Eco-environment

Impact of Climate Change and Human Activity on the Eco-environment

Vanadium-Based Nanomaterials for Electrochemical Energy Storage

Vanadium-Based Nanomaterials for Electrochemical Energy Storage

Multimodal Learning Toward Micro-Video Understanding

Multimodal Learning Toward Micro-Video Understanding

Learning from Multiple Social Networks

Learning from Multiple Social Networks

Graph Learning for Fashion Compatibility Modeling

Graph Learning for Fashion Compatibility Modeling

Compatibility Modeling

Compatibility Modeling

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