The 1st EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval

2025-04-21

AI Summary

To address the high computational overhead of representation learning in multimodal information retrieval (MIR) during training, deployment, and inference in the large-model era, this project proposes a systematic efficiency optimization framework. Methodologically, it introduces the first holistic efficiency–effectiveness evaluation framework tailored for multimodal retrieval, integrating techniques including knowledge distillation, modality pruning, quantization, sparse activation, and cross-modal lightweight alignment—adapted to foundational models such as CLIP and LLaMA. In terms of contributions, the project launched the inaugural EReL@MIR international workshop, establishing the first academic platform dedicated to efficiency in multimodal retrieval; it also released an open-source benchmark and a community-agreed framework, fostering standardization and practical adoption of efficient representation learning across academia and industry.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

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.

Problem

• Address efficiency challenges in multimodal representation learning for IR
• Optimize large pre-trained models for practical IR adaptation
• Develop evaluation metrics for efficient multimodal retrieval systems

Innovation

• Efficient multimodal representation learning techniques
• Optimizing large pre-trained foundation models
• Novel efficiency metrics and benchmarks