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MORE’24 Multimedia Object Re-ID: Advancements, Challenges, and Opportunities
Zheng, Zhedong1; Wang, Yaxiong2; Qian, Xuelin3; Zhong, Zhun4; Wang, Zheng5; Zheng, Liang6
2024-06-07
Conference Name2024 International Conference on Multimedia Retrieval, ICMR 2024
Source PublicationICMR 2024 - Proceedings of the 2024 International Conference on Multimedia Retrieval
IssueICMR '24: Proceedings of the 2024 International Conference on Multimedia Retrieval
Pages1336-1338
Conference DateJune 10 -14, 2024
Conference PlacePhuket
CountryThailand
PublisherASSOC COMPUTING MACHINERY1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES
Abstract

Object re-identification (or object re-id) has gained significant attention in recent years, fueled by the increasing demand for advanced video analysis and safety systems. In object re-id, a query can be of different modalities, such as an image, a video, or natural language, containing or describing the object of interest. This workshop aims to bring together researchers, practitioners, and enthusiasts interested in object re-id to delve into the latest advancements, challenges, and opportunities in this dynamic field. The workshop covers a spectrum of topics related to object re-id, including but not limited to deep metric learning, multi-view data generation, video-based object re-id, cross-domain object re-id and real-world applications. The workshop provides a platform for researchers to showcase their work, exchange ideas, and foster potential collaborations. Additionally, it serves as a valuable opportunity for practitioners to stay abreast of the latest developments in object re-id technology.

KeywordDeep Metric Learning Multi-view Generation Multimedia Retrieval Object Re-identification Representation Learning
DOI10.1145/3652583.3658892
URLView the original
Indexed ByCPCI-S
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS IDWOS:001282078400173
Scopus ID2-s2.0-85199214010
Fulltext Access
Citation statistics
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Faculty of Science and Technology, Institute of Collaborative Innovation, University of Macau, Macao
2.Hefei University of Technology, Hefei, China
3.Northwestern Polytechnical University, Xi’an, China
4.University of Nottingham, United Kingdom
5.Wuhan University, Wuhan, China
6.Australian National University, Australia
First Author AffilicationINSTITUTE OF COLLABORATIVE INNOVATION
Recommended Citation
GB/T 7714
Zheng, Zhedong,Wang, Yaxiong,Qian, Xuelin,et al. MORE’24 Multimedia Object Re-ID: Advancements, Challenges, and Opportunities[C]:ASSOC COMPUTING MACHINERY1601 Broadway, 10th Floor, NEW YORK, NY, UNITED STATES, 2024, 1336-1338.
APA Zheng, Zhedong., Wang, Yaxiong., Qian, Xuelin., Zhong, Zhun., Wang, Zheng., & Zheng, Liang (2024). MORE’24 Multimedia Object Re-ID: Advancements, Challenges, and Opportunities. ICMR 2024 - Proceedings of the 2024 International Conference on Multimedia Retrieval(ICMR '24: Proceedings of the 2024 International Conference on Multimedia Retrieval), 1336-1338.
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