Graph Learning for Fashion Compatibility Modeling

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 servedas 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.

• 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.