abstract

Out-of-distribution Generalization and Its Applications for Multimedia

Authors:

Wenwu ZhuAuthors Info & Claims

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

Pages 5681 - 5682

https://doi.org/10.1145/3474085.3478876

Published: 17 October 2021 Publication History

Abstract

Out-of-distribution generalization is becoming a hot research topic in both academia and industry. This tutorial is to disseminate and promote the recent research achievements on out-of-distribution generalization as well as their applications on multimedia, which is an exciting and fast-growing research direction in the general field of machine learning and multimedia. We will advocate novel, high-quality research findings, as well as innovative solutions to the challenging problems in out-of-distribution generalization and its applications for multimedia. This topic is at the core of the scope of ACM Multimedia, and is attractive to MM audience from both academia and industry.

References

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[2]

Yudong Chen, Xin Wang, Miao Fan, Jizhou Huang, Shengwen Yang, and Wenwu Zhu. Curriculum meta-learning for next poi recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 216--225, 2021.

Digital Library

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Chaoyu Guan, Ziwei Zhang, Haoyang Li, Heng Chang, Zeyang Zhang, Yijian Qin, Jiyan Jiang, Xin Wang, and Wenwu Zhu. Autogl: A library for automated graph learning. In ICLR Workshop on Geometrical and Topological Representation, 2021.

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Cited By

Kong MLi RWang JLi XJin SXie WHou MCao C(2024)CFTNet: a robust credit card fraud detection model enhanced by counterfactual data augmentationNeural Computing and Applications10.1007/s00521-024-09546-936:15(8607-8623)Online publication date: 26-Feb-2024
https://doi.org/10.1007/s00521-024-09546-9
Wang XChen HZhu WEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Disentangled Representation Learning for MultimediaProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3613859(9702-9704)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3613859

Index Terms

Out-of-distribution Generalization and Its Applications for Multimedia
1. Computing methodologies
  1. Machine learning

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Information & Contributors

Information

Published In

cover image ACM Conferences

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

October 2021

5796 pages

ISBN:9781450386517

DOI:10.1145/3474085

General Chairs:
Heng Tao Shen
University of Electronic Science&Technology of China, China
,
Yueting Zhuang
Zhejiang University, China
,
John R. Smith
IBM, USA
,
Program Chairs:
Yang Yang
University of Electronic Science and Technology of China, China
,
Pablo Cesar
CWI&TU Delft, The Netherlands
,
Florian Metze
FACEBOOK, Inc., USA
,
Balakrishnan Prabhakaran
University of Texas at Dallas, USA

Copyright © 2021 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

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Published: 17 October 2021

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MM '21

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SIGMM

MM '21: ACM Multimedia Conference

October 20 - 24, 2021

Virtual Event, China

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Kong MLi RWang JLi XJin SXie WHou MCao C(2024)CFTNet: a robust credit card fraud detection model enhanced by counterfactual data augmentationNeural Computing and Applications10.1007/s00521-024-09546-936:15(8607-8623)Online publication date: 26-Feb-2024
https://doi.org/10.1007/s00521-024-09546-9
Wang XChen HZhu WEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Disentangled Representation Learning for MultimediaProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3613859(9702-9704)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3613859

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