# LVP-M3: Language-aware Visual Prompt for Multilingual Multimodal Machine Translation

arXiv: https://arxiv.org/abs/2210.15461 · HTML: https://mlsanity.com/paper/2210.15461/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 58 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 30 of 47)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding](https://mlsanity.com/paper/2104.12763/) (2021)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training](https://mlsanity.com/paper/1908.06066/) (2019)
- [Large-Scale Adversarial Training for Vision-and-Language Representation Learning](https://mlsanity.com/paper/2006.06195/) (2020)
- [How Much Can CLIP Benefit Vision-and-Language Tasks?](https://mlsanity.com/paper/2107.06383/) (2021)
- [Dynamic Filter Networks](https://mlsanity.com/paper/1605.09673/) (2016)
- [How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models](https://mlsanity.com/paper/2012.15613/) (2020)
- [Conditional Convolutions for Instance Segmentation](https://mlsanity.com/paper/2003.05664/) (2020)
- [CondConv: Conditionally Parameterized Convolutions for Efficient Inference](https://mlsanity.com/paper/1904.04971/) (2019)
- [Multi30K: Multilingual English-German Image Descriptions](https://mlsanity.com/paper/1605.00459/) (2016)
- [Cross-Lingual Ability of Multilingual BERT: An Empirical Study](https://mlsanity.com/paper/1912.07840/) (2019)

## Built on by (most cited first, 21 of 21)

- [OWL: A Large Language Model for IT Operations](https://mlsanity.com/paper/2309.09298/) (2023)
- [IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web](https://mlsanity.com/paper/2409.18980/) (2024)
- [LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection](https://mlsanity.com/paper/2401.04749/) (2024)
- [E^2-LLM: Efficient and Extreme Length Extension of Large Language Models](https://mlsanity.com/paper/2401.06951/) (2024)
- [Mitigating Hallucinations in Large Vision-Language Models by Adaptively Constraining Information Flow](https://mlsanity.com/paper/2502.20750/) (2025)
- [M2C: Towards Automatic Multimodal Manga Complement](https://mlsanity.com/paper/2310.17130/) (2023)
- [Bridging the Gap between Synthetic and Authentic Images for Multimodal Machine Translation](https://mlsanity.com/paper/2310.13361/) (2023)
- [Leveraging Large Language Models for Enhanced NLP Task Performance through Knowledge Distillation and Optimized Training Strategies](https://mlsanity.com/paper/2402.09282/) (2024)
- [m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt](https://mlsanity.com/paper/2403.17556/) (2024)
- [A Survey on Multi-modal Machine Translation: Tasks, Methods and Challenges](https://mlsanity.com/paper/2405.12669/) (2024)
- [SVIPTR: Fast and Efficient Scene Text Recognition with Vision Permutable Extractor](https://mlsanity.com/paper/2401.10110/) (2024)
- [MultiScript30k: Leveraging Multilingual Embeddings to Extend Cross Script Parallel Data](https://mlsanity.com/paper/2512.11074/) (2025)
- [Multilingual Entity and Relation Extraction from Unified to Language-specific Training](https://mlsanity.com/paper/2301.04434/) (2023)
- [MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction](https://mlsanity.com/paper/2308.06552/) (2023)
- [Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels](https://mlsanity.com/paper/2307.01969/) (2023)
- [Context-Informed Machine Translation of Manga using Multimodal Large Language Models](https://mlsanity.com/paper/2411.02589/) (2024)
- [VIDA: A Dataset for Visually Dependent Ambiguity in Multimodal Machine Translation](https://mlsanity.com/paper/2605.02035/) (2026)
- [MLAD: A Unified Model for Multi-system Log Anomaly Detection](https://mlsanity.com/paper/2401.07655/) (2024)
- [LLaVA-NeuMT: Selective Layer-Neuron Modulation for Efficient Multilingual Multimodal Translation](https://mlsanity.com/paper/2507.18940/) (2025)
- [Scalable Multilingual Multimodal Machine Translation with Speech-Text Fusion](https://mlsanity.com/paper/2602.21646/) (2026)
- [Improving Visual Sensitivity of LLMs on Multimodal Machine Translation with Metric-based Loss Weighting](https://mlsanity.com/paper/2609.31169/) (2026)

## Abstract (arXiv)

Multimodal Machine Translation (MMT) focuses on enhancing text-only translation with visual features, which has attracted considerable attention from both natural language processing and computer vision communities. Recent advances still struggle to train a separate model for each language pair, which is costly and unaffordable when the number of languages increases in the real world. In other words, the multilingual multimodal machine translation (Multilingual MMT) task has not been investigated, which aims to handle the aforementioned issues by providing a shared semantic space for multiple languages. Besides, the image modality has no language boundaries, which is superior to bridging the semantic gap between languages. To this end, we first propose the Multilingual MMT task by establishing two new Multilingual MMT benchmark datasets covering seven languages. Then, an effective baseline LVP-M3 using visual prompts is proposed to support translations between different languages, which includes three stages (token encoding, language-aware visual prompt generation, and language translation). Extensive experimental results on our constructed benchmark datasets demonstrate the effectiveness of LVP-M3 method for Multilingual MMT.