The 2nd EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval

• URL: http://arxiv.org/abs/2605.26941v1
• Date: Tue, 26 May 2026 12:31:48 GMT
• Title: The 2nd EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval
• Abstract summary: EReL@MIR aims to bring together researchers from academia and industry to discuss emerging solutions, open challenges, and new efficiency metrics and benchmarks for representation learning in the foundation-model era.<n>We propose organizing the EReL@MIR workshop at MM 2026, bringing together researchers from academia and industry to discuss emerging solutions, open challenges, and new efficiency metrics and benchmarks for representation learning in the foundation-model era.
• Score: 51.801765084436646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal representation learning has attracted increasing attention in AI, driven by the strong performance of large, pretrained multimodal foundation models such as Qwen, LLaVA, and CLIP. These models deliver impressive performance on a range of multimodal information retrieval (MIR) tasks, including web search, cross-modal retrieval, and recommender systems. Yet their massive parameter counts create major efficiency bottlenecks when adapting their representations for IR tasks during training, deployment, and inference. These limitations hinder the practical use of foundation models for representation learning in information retrieval. To address these issues, we propose organizing the EReL@MIR workshop at MM 2026, bringing together researchers from academia and industry to discuss emerging solutions, open challenges, and new efficiency metrics and benchmarks for multimodal IR representation learning in the foundation-model era. The workshop's official website is available at https://erel-mir.github.io/.

Related papers

• Feature Fusion Revisited: Multimodal CTR Prediction for MMCTR Challenge [4.3058911704400415]
  The EReL@MIR workshop provided a valuable opportunity to experiment with various approaches aimed at improving the efficiency of multimodal representation learning.<n>Our team was honored to receive the award for Task 2 - Winner (Multimodal CTR Prediction)
  arXiv  Detail & Related papers  (2025-04-26T16:04:33Z)
• The 1st EReL@MIR Workshop on Efficient Representation Learning for Multimodal Information Retrieval [49.587042083937426]
  We propose the first EReL@MIR workshop at the Web Conference 2025, inviting participants to explore novel solutions.<n>This workshop aims to provide a platform for both academic and industry researchers to engage in discussions, share insights, and foster collaboration.
  arXiv  Detail & Related papers  (2025-04-21T01:10:59Z)
• LamRA: Large Multimodal Model as Your Advanced Retrieval Assistant [63.28378110792787]
  We introduce LamRA, a versatile framework designed to empower Large Multimodal Models with sophisticated retrieval and reranking capabilities.<n>For retrieval, we adopt a two-stage training strategy comprising language-only pre-training and multimodal instruction tuning.<n>For reranking, we employ joint training for both pointwise and listwise reranking, offering two distinct ways to further boost the retrieval performance.
  arXiv  Detail & Related papers  (2024-12-02T17:10:16Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• EmbedLLM: Learning Compact Representations of Large Language Models [28.49433308281983]
  We propose EmbedLLM, a framework designed to learn compact vector representations of Large Language Models. We introduce an encoder-decoder approach for learning such embeddings, along with a systematic framework to evaluate their effectiveness. Empirical results show that EmbedLLM outperforms prior methods in model routing both in accuracy and latency.
  arXiv  Detail & Related papers  (2024-10-03T05:43:24Z)
• NoteLLM-2: Multimodal Large Representation Models for Recommendation [71.87790090964734]
  Large Language Models (LLMs) have demonstrated exceptional proficiency in text understanding and embedding tasks.<n>Their potential in multimodal representation, particularly for item-to-item (I2I) recommendations, remains underexplored.<n>We propose an end-to-end fine-tuning method that customizes the integration of any existing LLMs and vision encoders for efficient multimodal representation.
  arXiv  Detail & Related papers  (2024-05-27T03:24:01Z)
• Generative Multimodal Models are In-Context Learners [60.50927925426832]
  We introduce Emu2, a generative multimodal model with 37 billion parameters, trained on large-scale multimodal sequences. Emu2 exhibits strong multimodal in-context learning abilities, even emerging to solve tasks that require on-the-fly reasoning.
  arXiv  Detail & Related papers  (2023-12-20T18:59:58Z)
• How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents [9.840104333194663]
  We argue for hierarchy in the design of representation models and contribute with a novel multimodal representation model, MUSE. MUSE is the sensory representation model of deep reinforcement learning agents provided with multimodal observations in Atari games. We perform a comparative study over different designs of reinforcement learning agents, showing that MUSE allows agents to perform tasks under incomplete perceptual experience with minimal performance loss.
  arXiv  Detail & Related papers  (2021-10-07T16:35:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.