# Multilingual Image Description with Neural Sequence Models

arXiv: https://arxiv.org/abs/1510.04709 · HTML: https://mlsanity.com/paper/1510.04709/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2015.

## Built on by (most cited first, 6 of 6)

- [From Show to Tell: A Survey on Deep Learning-based Image Captioning](https://mlsanity.com/paper/2107.06912/) (2021)
- [Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation](https://mlsanity.com/paper/2105.14462/) (2021)
- [Image Captioning via Compact Bidirectional Architecture](https://mlsanity.com/paper/2201.01984/) (2022)
- [Embedded Heterogeneous Attention Transformer for Cross-lingual Image Captioning](https://mlsanity.com/paper/2307.09915/) (2023)
- [Exploring the Necessity of Visual Modality in Multimodal Machine Translation using Authentic Datasets](https://mlsanity.com/paper/2404.06107/) (2024)
- [Video-guided Machine Translation with Global Video Context](https://mlsanity.com/paper/2604.06789/) (2026)

## Abstract (arXiv)

In this paper we present an approach to multi-language image description bringing together insights from neural machine translation and neural image description. To create a description of an image for a given target language, our sequence generation models condition on feature vectors from the image, the description from the source language, and/or a multimodal vector computed over the image and a description in the source language. In image description experiments on the IAPR-TC12 dataset of images aligned with English and German sentences, we find significant and substantial improvements in BLEU4 and Meteor scores for models trained over multiple languages, compared to a monolingual baseline.