Graph Learning for Fashion Compatibility Modeling

ISBN-13: 9783031188169 / Angielski / Twarda / 2022 / 112 str.

Weili Guan; Xuemeng Song; Xiaojun Chang

Opis

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.  

Spis treści

Introduction.- Correlation-oriented Graph Learning for OCM.- Modality-oriented Graph Learning for OCM.- Unsupervised Disentangled Graph Learning for OCM.- Supervised Disentangled Graph Learning for OCM.- Heterogeneous Graph Learning for Personalized OCM.- Research Frontiers.

O autorze

Weili Guan received a master degree from National University of Singapore. After that, she joined Hewlett Packard Enterprise in Singapore as a Software Engineer and worked there for several years.  She is currently a PhD student with the Faculty of Information Technology, Monash University (Clayton Campus), Australia. Her research interests are multimedia computing and information retrieval. She has authored or co-authored more than 30 papers at first-tier conferences and journals, like ACM MM, SIGIR, and IEEE TIP.

Xuemeng Song received a B.E. from the University of Science and Technology of China in 2012, and a Ph.D. from the School of Computing, National University of Singapore in 2016. She is currently an Associate Professor of Shandong University, Jinan, China. Her research interests include the information retrieval and social network analysis. She has published several papers in top venues, such as ACM SIGIR, MM, TIP, and TOIS. In addition, she has served as a reviewer for many top conferences and journals.

Dr. Xiaojun Chang is a Professor at the Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney.  He is the Director of The ReLER Lab. He is also an Honorary Professor in the School of Computing Technologies, RMIT University, Australia. Before joining UTS, he was an Associate Professor at School of Computing Technologies, RMIT University, Australia. After graduation, he subsequently worked as a Postdoc Research Fellow at School of Computer Science, Carnegie Mellon University, Lecturer and Senior Lecturer in the Faculty of Information Technology, Monash University, Australia.  He has focused his research on exploring multiple signals (visual, acoustic, textual) for automatic content analysis in unconstrained or surveillance videos.  His team has won multiple prizes from international grand challenges which hosted competitive teams from MIT, University of Maryland, Facebook AI Research (FAIR) and Baidu VIS, and aim to advance visual understanding using deep learning. For example, he won the first place in the TrecVID 2019 - Activity Extended Video (ActEV) challenge, which was held by National Institute of Standards and Technology, US.

Liqiang Nie, Ph.D., is Dean with the Department of Computer Science and Technology at Harbin Institute of Technology (Shenzhen).  He received his B.Eng. and Ph.D. degrees from Xi'an Jiaotong University and National University of Singapore (NUS), respectively. His research interests lie primarily in multimedia computing and information retrieval.  Dr. Nie has co-/authored more than 100 papers and four books and has received more than 15,000 Google Scholar citations.  He is an Associate Editor of IEEE TKDE, IEEE TMM, IEEE TCSVT, ACM ToMM, and Information Science. He is also a regular area chair of ACM MM, NeurIPS, IJCAI, and AAAI and a member of ICME steering committee.  Dr. Nie has received many awards, including ACM MM and SIGIR best paper honorable mention in 2019, SIGMM rising star in 2020, TR35 China 2020, DAMO Academy Young Fellow in 2020, and SIGIR best student paper in 2021.

Blurb

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 gives 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.