# Universal Vision-Language Dense Retrieval: Learning A Unified Representation Space for Multi-Modal Retrieval

arXiv: https://arxiv.org/abs/2209.00179 · HTML: https://mlsanity.com/paper/2209.00179/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2022.

## Built on by (most cited first, 30 of 58)

- [GME: Improving Universal Multimodal Retrieval by Multimodal LLMs](https://mlsanity.com/paper/2412.16855/) (2024)
- [UniIR: Training and Benchmarking Universal Multimodal Information Retrievers](https://mlsanity.com/paper/2311.17136/) (2023)
- [MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs](https://mlsanity.com/paper/2411.02571/) (2024)
- [E5-V: Universal Embeddings with Multimodal Large Language Models](https://mlsanity.com/paper/2407.12580/) (2024)
- [MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval](https://mlsanity.com/paper/2412.14475/) (2024)
- [VISTA: Visualized Text Embedding For Universal Multi-Modal Retrieval](https://mlsanity.com/paper/2406.04292/) (2024)
- [UniBind: LLM-Augmented Unified and Balanced Representation Space to Bind Them All](https://mlsanity.com/paper/2403.12532/) (2024)
- [mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data](https://mlsanity.com/paper/2502.08468/) (2025)
- [Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.08826/) (2025)
- [A Survey of Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.08748/) (2025)
- [Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language Models](https://mlsanity.com/paper/2501.15140/) (2025)
- [OmniBind: Teach to Build Unequal-Scale Modality Interaction for Omni-Bind of All](https://mlsanity.com/paper/2405.16108/) (2024)
- [RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning](https://mlsanity.com/paper/2502.00848/) (2025)
- [WavRAG: Audio-Integrated Retrieval Augmented Generation for Spoken Dialogue Models](https://mlsanity.com/paper/2502.14727/) (2025)
- [Cross-modal Retrieval for Knowledge-based Visual Question Answering](https://mlsanity.com/paper/2401.05736/) (2024)
- [Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts](https://mlsanity.com/paper/2502.17297/) (2025)
- [MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model](https://mlsanity.com/paper/2310.13265/) (2023)
- [U-MARVEL: Unveiling Key Factors for Universal Multimodal Retrieval via Embedding Learning with MLLMs](https://mlsanity.com/paper/2507.14902/) (2025)
- [UniECS: Unified Multimodal E-Commerce Search Framework with Gated Cross-modal Fusion](https://mlsanity.com/paper/2508.13843/) (2025)
- [Image Anything: Towards Reasoning-coherent and Training-free Multi-modal Image Generation](https://mlsanity.com/paper/2401.17664/) (2024)
- [See-Saw Modality Balance: See Gradient, and Sew Impaired Vision-Language Balance to Mitigate Dominant Modality Bias](https://mlsanity.com/paper/2503.13834/) (2025)
- [Synthetic Multimodal Question Generation](https://mlsanity.com/paper/2407.02233/) (2024)
- [HIPPO: Enhancing the Table Understanding Capability of LLMs through Hybrid-Modal Preference Optimization](https://mlsanity.com/paper/2502.17315/) (2025)
- [Joint Fusion and Encoding: Advancing Multimodal Retrieval from the Ground Up](https://mlsanity.com/paper/2502.20008/) (2025)
- [Supervised Fine-Tuning or Contrastive Learning? Towards Better Multimodal LLM Reranking](https://mlsanity.com/paper/2510.14824/) (2025)
- [Building A Coding Assistant via the Retrieval-Augmented Language Model](https://mlsanity.com/paper/2410.16229/) (2024)
- [Availability-aware Sensor Fusion via Unified Canonical Space](https://mlsanity.com/paper/2503.07029/) (2025)
- [GENIUS: A Generative Framework for Universal Multimodal Search](https://mlsanity.com/paper/2503.19868/) (2025)
- [ChartCards: A Chart-Metadata Generation Framework for Multi-Task Chart Understanding](https://mlsanity.com/paper/2505.15046/) (2025)
- [From Generator to Embedder: Harnessing Innate Abilities of Multimodal LLMs via Building Zero-Shot Discriminative Embedding Model](https://mlsanity.com/paper/2508.00955/) (2025)

## Abstract (arXiv)

This paper presents Universal Vision-Language Dense Retrieval (UniVL-DR), which builds a unified model for multi-modal retrieval. UniVL-DR encodes queries and multi-modality resources in an embedding space for searching candidates from different modalities. To learn a unified embedding space for multi-modal retrieval, UniVL-DR proposes two techniques: 1) Universal embedding optimization strategy, which contrastively optimizes the embedding space using the modality-balanced hard negatives; 2) Image verbalization method, which bridges the modality gap between images and texts in the raw data space. UniVL-DR achieves the state-of-the-art on the multi-modal open-domain question answering benchmark, WebQA, and outperforms all retrieval models on the two subtasks, text-text retrieval and text-image retrieval. It demonstrates that universal multi-modal search is feasible to replace the divide-and-conquer pipeline with a united model and also benefits single/cross modality tasks. All source codes of this work are available at https://github.com/OpenMatch/UniVL-DR.