Multi-modal Hash Learning: Efficient Multimedia Retrieval and Recommendations (Synthesis Lectures on Information Concepts, Retrieval, and Services)

Zhu, Lei; Li, Jingjing; Guan, Weili

ISBN 10: 303137293X / ISBN 13: 9783031372933

Verlag: Springer, 2024

Beschreibung

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Bestandsnummer des Verkäufers 18402823728

Bibliografische Details

• Titel: Multi-modal Hash Learning: Efficient Multimedia Retrieval and Recommendations (Synthesis Lectures on Information Concepts, Retrieval, and Services)
• Verlag: Springer
• Erscheinungsjahr: 2024
• Sprache: Englisch
• ISBN-10: 303137293X
• ISBN-13: 9783031372933
• Einband: Softcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

This book systemically presents key concepts of multi-modal hashing technology, recent advances on large-scale efficient multimedia search and recommendation, and recent achievements in multimedia indexing technology.

Über die Autorin bzw. den Autor

Lei Zhu, Ph.D. is a Professor in the School of Information Science and Engineering, Shandong Normal University. He received his B.Eng. and Ph.D. degrees from Wuhan University of Technology and Huazhong University Science and Technology, respectively. He was a Research Fellow at the University of Queensland (2016-2017). His research interests include large-scale multimedia content analysis and retrieval. 

Jingjing Li, Ph.D, is a Professor in the School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC). He received his B.Eng., M.Sc. and Ph.D. degrees from UESTC. His research interests include domain adaptation and zero-shot learning. 

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, such as ACM MM, SIGIR, and IEEE TIP.