Motivation

Multimodal representation learning has garnered significant attention in the AI community, largely due to the success of large pre-trained multimodal foundation models like LLaMA, GPT, Mistral, and CLIP. These models have achieved remarkable performance across various tasks of multimodal information retrieval (MIR), including web search, cross-modal retrieval, and recommender systems, etc. However, due to their enormous parameter sizes, significant efficiency challenges emerge across training, deployment, and inference stages when adapting these models’ representation for IR tasks. These challenges present substantial obstacles to the practical adaptation of foundation models for representation learning in information retrieval tasks.

To address these pressing issues, we propose organizing the first EReL@MIR workshop at the Web Conference 2025, inviting participants to explore novel solutions, emerging problems, challenges, efficiency evaluation metrics and benchmarks. This workshop aims to provide a platform for both academic and industry researchers to engage in discussions, share insights, and foster collaboration toward achieving efficient and effective representation learning for multimodal information retrieval in the era of large foundation models.

Call for Papers

We invite researchers to submit their latest work to the EReL@MIR Workshop on fundamental challenges in multimodal representation learning for Multimodal Information Retrieval (MIR). The topics of interest include, but are not limited to:

- Efficient Multimodal Representation Adaptation based on Multimodal Foundation Models

- Data-Efficiency in Multimodal Representation Learning

- Efficient Multimodal Fusion for Representation Learning

- Efficient Cross-Modality Interaction for MIR

- Real-Time Inference for Multimodal Representations

- Efficient MIR Foundation Models

- Benchmarks and Metrics for Multimodal Representation Learning Efficiency

Submission Guidelines

Submissions of papers must be at least 4 pages and at most 8 pages (including figures, tables, proofs, appendixes, acknowledgments, and any content except references) in length, with unlimited pages for references. Submissions of papers must be in English, in PDF format, in the current ACM two-column conference format. Suitable LaTeX, Word, and Overleaf templates are available from the ACM Website (use the “sigconf” proceedings template for LaTeX and the Interim Template for Word).

The review process of the submitted manuscripts will be done together with our program committee. The selection will depend on the technical soundness and relevance of submissions to the community that the workshop is targeting. The submission website will be announced soon.

WWW2025 Fast Track - Papers rejected and/or withdrawn from WWW 2025 that wish to be submitted to EReL@MIR can use the same submission link. These submissions should include the review comments in an appendix. Such papers will bypass the peer-review process, with acceptance decisions made directly by the meta-reviewers.

Acceptance and the Best Paper Award

Authors of accepted papers may choose whether to include their work in the WWW’25 Companion proceedings. We will reach out to the authors of accepted papers at a later time to facilitate this decision. The committee will review the submissions and select one outstanding workshop paper to receive the Best Paper Award at the EReL@MIR Workshop.

Committees

Organizing Committee

• Junchen Fu, University of Glasgow

• Xuri Ge, Shandong University and University of Glasgow

• Xin Xin, Shandong University

• Haitao Yu, University of Tsukuba

• Yue Feng, University of Birmingham

• Alexandros Karatzoglou, Amazon

• Ioannis Arapakis, Telefónica Scientific Research

• Joemon M. Jose, University of Glasgow

Program Committee

• Hui Li, Xiamen University

• Qian Li, Beijing University of Posts and Telecommunications

• Siwei liu, University of Aberdeen

• Songpei Xu, University of Glasgow

• Xi Wang, University of Sheffield

• Jiayi Ji, National University of Singapore and Xiamen University

• Hengchang Hu, National University of Singapore

• Fuhai Chen, Fuzhou University

• Mingyue Cheng, University of Science and Technology of China

Contact

All questions about submissions should be emailed to j.fu.3@research.gla.ac.uk; xurigexmu@gmail.com; joemon.jose@glasgow.ac.uk