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ELVA framework tackles "grain blindness" in multimodal retrieval · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced ELVA, a novel framework designed to address "grain blindness" in Universal Multimodal Retrieval (UMR) systems that utilize Multimodal Large Language Models (MLLMs). Grain blindness occurs when models overlook fine-grained information in queries, treating all negative samples equally. ELVA employs a rule-based Reinforcement Learning with Verifiable Rewards (RLVR) approach to optimize the ranking of negative samples and increase the similarity gap between positive and negative samples. To evaluate its effectiveness, a new benchmark called MRBench was developed, and ELVA demonstrated state-of-the-art results, including a significant 13.1% improvement on MRBench. AI

IMPACT This research could improve the accuracy and nuance of multimodal retrieval systems, leading to more sophisticated search and information access capabilities.

RANK_REASON The cluster contains a research paper detailing a new framework and benchmark for multimodal retrieval.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

ELVA framework tackles "grain blindness" in multimodal retrieval · 2 sources tracked

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The cluster contains a research paper detailing a new framework and benchmark for multimodal retrieval.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yuhan Liu, Pei Fu, Hang Li, Yukun Qi, Chao Jiang, Jingwen Fu, Zhen Liu, Bin Qin, Zhenbo Luo, Jian Luan, Jingmin Xin ·

    ELVA: Exploring Ranking-Driven Universal Multimodal Retrieval

    arXiv:2606.20280v1 Announce Type: cross Abstract: Leveraging Multimodal Large Language Models (MLLMs) via contrastive learning has become a mainstream paradigm for improving the performance of Universal Multimodal Retrieval (UMR). However, previous works have ignored the grain bl…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jingmin Xin ·

    ELVA: Exploring Ranking-Driven Universal Multimodal Retrieval

    Leveraging Multimodal Large Language Models (MLLMs) via contrastive learning has become a mainstream paradigm for improving the performance of Universal Multimodal Retrieval (UMR). However, previous works have ignored the grain blindness when adapting the contrastive paradigm int…

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