M3DR: Towards Universal Multilingual Multimodal Document Retrieval

• URL: http://arxiv.org/abs/2512.03514v1
• Date: Wed, 03 Dec 2025 07:17:59 GMT
• Title: M3DR: Towards Universal Multilingual Multimodal Document Retrieval
• Authors: Adithya S Kolavi, Vyoman Jain,
• Abstract summary: We present M3DR (Multilingual Multimodal Document Retrieval), a framework designed to bridge this gap across languages.<n>We generalize across different vision-language architectures and model sizes, enabling robust cross-lingual and cross-modal alignment.<n>Our models, NetraEmbed and ColNetraEmbed achieve state-of-the-art performance with 150% relative improvements on cross-lingual retrieval.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal document retrieval systems have shown strong progress in aligning visual and textual content for semantic search. However, most existing approaches remain heavily English-centric, limiting their effectiveness in multilingual contexts. In this work, we present M3DR (Multilingual Multimodal Document Retrieval), a framework designed to bridge this gap across languages, enabling applicability across diverse linguistic and cultural contexts. M3DR leverages synthetic multilingual document data and generalizes across different vision-language architectures and model sizes, enabling robust cross-lingual and cross-modal alignment. Using contrastive training, our models learn unified representations for text and document images that transfer effectively across languages. We validate this capability on 22 typologically diverse languages, demonstrating consistent performance and adaptability across linguistic and script variations. We further introduce a comprehensive benchmark that captures real-world multilingual scenarios, evaluating models under monolingual, multilingual, and mixed-language settings. M3DR generalizes across both single dense vector and ColBERT-style token-level multi-vector retrieval paradigms. Our models, NetraEmbed and ColNetraEmbed achieve state-of-the-art performance with ~150% relative improvements on cross-lingual retrieval.

Related papers

• uCLIP: Parameter-Efficient Multilingual Extension of Vision-Language Models with Unpaired Data [3.364569898365253]
  We propose a lightweight and data-efficient framework for multilingual vision-language alignment.<n>Our approach requires no image-text pairs or text-text pairs and freezes both the pretrained image encoder and multilingual text encoder during training.<n>This minimal training setup enables robust multilingual alignment even for languages with limited supervision.
  arXiv  Detail & Related papers  (2025-11-17T06:34:49Z)
• m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt [39.2728779674405]
  We propose a framework to leverage the multimodal prompt to guide the Multimodal Multilingual neural Machine Translation (m3P) Our method aims to minimize the representation distance of different languages by regarding the image as a central language. Experimental results show that m3P outperforms previous text-only baselines and multilingual multimodal methods by a large margin.
  arXiv  Detail & Related papers  (2024-03-26T10:04:24Z)
• LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation [94.33019040320507]
  Multimodal Machine Translation (MMT) focuses on enhancing text-only translation with visual features. Recent advances still struggle to train a separate model for each language pair, which is costly and unaffordable when the number of languages increases. We propose the Multilingual MMT task by establishing two new Multilingual MMT benchmark datasets covering seven languages.
  arXiv  Detail & Related papers  (2022-10-19T12:21:39Z)
• xGQA: Cross-Lingual Visual Question Answering [100.35229218735938]
  xGQA is a new multilingual evaluation benchmark for the visual question answering task. We extend the established English GQA dataset to 7 typologically diverse languages. We propose new adapter-based approaches to adapt multimodal transformer-based models to become multilingual.
  arXiv  Detail & Related papers  (2021-09-13T15:58:21Z)
• UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training [52.852163987208826]
  UC2 is the first machine translation-augmented framework for cross-lingual cross-modal representation learning. We propose two novel pre-training tasks, namely Masked Region-to-Token Modeling (MRTM) and Visual Translation Language Modeling (VTLM) Our proposed framework achieves new state-of-the-art on diverse non-English benchmarks while maintaining comparable performance to monolingual pre-trained models on English tasks.
  arXiv  Detail & Related papers  (2021-04-01T08:30:53Z)
• Are Multilingual Models Effective in Code-Switching? [57.78477547424949]
  We study the effectiveness of multilingual language models to understand their capability and adaptability to the mixed-language setting. Our findings suggest that pre-trained multilingual models do not necessarily guarantee high-quality representations on code-switching.
  arXiv  Detail & Related papers  (2021-03-24T16:20:02Z)
• Scalable Cross-lingual Document Similarity through Language-specific Concept Hierarchies [0.0]
  This paper presents an unsupervised document similarity algorithm that does not require parallel or comparable corpora. The algorithm annotates topics automatically created from documents in a single language with cross-lingual labels. Experiments performed on the English, Spanish and French editions of JCR-Acquis corpora reveal promising results on classifying and sorting documents by similar content.
  arXiv  Detail & Related papers  (2020-12-15T10:42:40Z)
• M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training [119.16007395162431]
  M3P is a Multilingual Multimodal Pre-trained model that combines multilingual pre-training and multimodal pre-training. We show that M3P can achieve comparable results for English and new state-of-the-art results for non-English languages.
  arXiv  Detail & Related papers  (2020-06-04T03:54:29Z)
• Learning to Scale Multilingual Representations for Vision-Language Tasks [51.27839182889422]
  The effectiveness of SMALR is demonstrated with ten diverse languages, over twice the number supported in vision-language tasks to date. We evaluate on multilingual image-sentence retrieval and outperform prior work by 3-4% with less than 1/5th the training parameters compared to other word embedding methods.
  arXiv  Detail & Related papers  (2020-04-09T01:03:44Z)

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.