MORE 2024

Workshop on

Multimedia Object Re-ID: Advancements, Challenges, and Opportunities (MORE 2024)

The accept papers will be published at ACM ICMR Workshop (top 50%), and go through the same peer review process as the regular papers. Several authors will be invited to do a oral presentation.

[Accepted Workshop Proposal] [Submission Site]

News

• Good papers will be recommended to ACM TOMM Special Issue. (Re-submittion is needed.)
• Paper submission site is open.

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-identification, 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-identification to delve into the latest advancements, challenges, and opportunities in this dynamic field. The workshop covers a spectrum of topics related to object re-identification, including but not limited to deep metric learning, multi-view data generation, video-based object re-identification, cross-domain object re-identification 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-identification technology. Overall, this workshop creates a unique space to explore the rapidly evolving field of object re-identification and its profound impact on advancing the capabilities of multimedia analysis and retrieval.

Key Words Multimedia Retrieval, Object Re-identification, Representation Learning, Deep Metric Learning, Multi-view Generation

The list of possible topics includes, but is not limited to:

• New Datasets and Benchmarks
• Deep Metric Learning
• Multi-view Data Generation
• Video-based Object Re-identification
• Cross-domain Object Re-identification
• Object Re-identification Domain Adaptation / Generalization
• Single/ Multiple Object Tracking
• Object Geo-localization
• Multimedia Re-ranking

Submission

Submission Site is at OpenReview

Submission template can be found at ACM or you may directly follow the overleaf template.

Submission Type

(1). Original papers (up to 4 pages in length, plus unlimited pages for references): original solution to the tasks in the scope of workshop topics and themes.

(2). Position or perspective papers (up to 4 pages in length, plus unlimited pages for references): original ideas, perspectives, research vision, and open challenges in the area of evaluation approaches for explainable multimedia systems;

(3). Survey papers (up to 4 pages in length, plus unlimited pages for references): papers summarizing existing publications in leading conferences and high-impact journals that are relevant for the topic of the workshop; Page limits include diagrams and appendices.

Submissions should be single-blind due to limited publication time, written in English, and formatted according to the current ACM two-column conference format. Suitable LaTeX, Word, and Overleaf templates are available from the ACM Website (use “sigconf” proceedings template for LaTeX and the Interim Template for Word).

Important Dates

Submission of papers:

• Workshop Papers Submission: 16 April 2024
• Workshop Papers Notification: 22 April 2024
• Camera-ready Submission: 25 April, 2024
• Student Travel Grant DDL： 25 April, 2024 https://icmr2024.org/student-travel-grants.html
• Conference Dates: 11 June 2024 – 13 June 2024

Please note: The submission deadline is at 11:59 p.m. of the stated deadline date Anywhere on Earth

Tips:

• For privacy protection, please blur faces in the published materials (such as paper, video, poster, etc.)
• For social good, please do not contain any misleading words, such as surveillance and secret.

Organizing Team

Zhedong Zheng, University of Macau, China	Yaxiong Wang, Hefei University of Technology, China	Xuelin Qian, Northwestern Polytechnical University, China
Zhun Zhong, University of Nottingham, United Kingdom	Zheng Wang, Wuhan University, China	Liang Zheng, Australian National University, Australia

Workshop Citation

@inproceedings{zheng2024MORE,
  title={MORE'24 Multimedia Object Re-ID: Advancements, Challenges, and Opportunities},
  author={Zheng, Zhedong and Wang, Yaxiong and Qian, Xuelin, and Zhong, Zhun and Wang, Zheng and Zheng, Liang},
  booktitle={ACM International Conference on Multimedia Retrieval Workshop},
  year={2024}
}