Residential College | false |
Status | 已發表Published |
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 Name | 2024 International Conference on Multimedia Retrieval, ICMR 2024 |
Source Publication | ICMR 2024 - Proceedings of the 2024 International Conference on Multimedia Retrieval |
Issue | ICMR '24: Proceedings of the 2024 International Conference on Multimedia Retrieval |
Pages | 1336-1338 |
Conference Date | June 10 -14, 2024 |
Conference Place | Phuket |
Country | Thailand |
Publisher | ASSOC 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. |
Keyword | Deep Metric Learning Multi-view Generation Multimedia Retrieval Object Re-identification Representation Learning |
DOI | 10.1145/3652583.3658892 |
URL | View the original |
Indexed By | CPCI-S |
Language | 英語English |
WOS Research Area | Computer Science |
WOS Subject | Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods |
WOS ID | WOS:001282078400173 |
Scopus ID | 2-s2.0-85199214010 |
Fulltext Access | |
Citation statistics | |
Document Type | Conference paper |
Collection | DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE |
Affiliation | 1.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 Affilication | INSTITUTE 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. |
Files in This Item: | There are no files associated with this item. |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment