# Unpaired Image Captioning by Language Pivoting

arXiv: https://arxiv.org/abs/1803.05526 · HTML: https://mlsanity.com/paper/1803.05526/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2018.

## Built on by (most cited first, 27 of 27)

- [VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning](https://mlsanity.com/paper/2102.10407/) (2021)
- [From Show to Tell: A Survey on Deep Learning-based Image Captioning](https://mlsanity.com/paper/2107.06912/) (2021)
- [VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix](https://mlsanity.com/paper/2206.08919/) (2022)
- [Deep Learning Approaches on Image Captioning: A Review](https://mlsanity.com/paper/2201.12944/) (2022)
- [MAGIC: Multimodal relAtional Graph adversarIal inferenCe for Diverse and Unpaired Text-based Image Captioning](https://mlsanity.com/paper/2112.06558/) (2021)
- [LMCap: Few-shot Multilingual Image Captioning by Retrieval Augmented Language Model Prompting](https://mlsanity.com/paper/2305.19821/) (2023)
- [Cross2StrA: Unpaired Cross-lingual Image Captioning with Cross-lingual Cross-modal Structure-pivoted Alignment](https://mlsanity.com/paper/2305.12260/) (2023)
- [Removing Word-Level Spurious Alignment between Images and Pseudo-Captions in Unsupervised Image Captioning](https://mlsanity.com/paper/2104.13872/) (2021)
- [Aligning Source Visual and Target Language Domains for Unpaired Video Captioning](https://mlsanity.com/paper/2211.12148/) (2022)
- [CLEAR: Improving Vision-Language Navigation with Cross-Lingual, Environment-Agnostic Representations](https://mlsanity.com/paper/2207.02185/) (2022)
- [CgT-GAN: CLIP-guided Text GAN for Image Captioning](https://mlsanity.com/paper/2308.12045/) (2023)
- [Object-Centric Unsupervised Image Captioning](https://mlsanity.com/paper/2112.00969/) (2021)
- [MedRAT: Unpaired Medical Report Generation via Auxiliary Tasks](https://mlsanity.com/paper/2407.03919/) (2024)
- [Unpaired Image Captioning by Image-level Weakly-Supervised Visual Concept Recognition](https://mlsanity.com/paper/2203.03195/) (2022)
- [Exploring Semantic Relationships for Unpaired Image Captioning](https://mlsanity.com/paper/2106.10658/) (2021)
- [Multimodal Data Augmentation for Image Captioning using Diffusion Models](https://mlsanity.com/paper/2305.01855/) (2023)
- ["Wikily" Supervised Neural Translation Tailored to Cross-Lingual Tasks](https://mlsanity.com/paper/2104.08384/) (2021)
- [DescribeEarth: Describe Anything for Remote Sensing Images](https://mlsanity.com/paper/2509.25654/) (2025)
- [Semi-Supervised Image Captioning by Adversarially Propagating Labeled Data](https://mlsanity.com/paper/2301.11174/) (2023)
- [ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation](https://mlsanity.com/paper/2303.06458/) (2023)
- [Emergence of Machine Language: Towards Symbolic Intelligence with Neural Networks](https://mlsanity.com/paper/2201.05489/) (2022)
- [Multilingual Multimodality: A Taxonomical Survey of Datasets, Techniques, Challenges and Opportunities](https://mlsanity.com/paper/2210.16960/) (2022)
- [Embedded Heterogeneous Attention Transformer for Cross-lingual Image Captioning](https://mlsanity.com/paper/2307.09915/) (2023)
- [Surveying the Landscape of Image Captioning Evaluation: A Comprehensive Taxonomy, Trends and Metrics Analysis](https://mlsanity.com/paper/2408.04909/) (2024)
- [Align Where the Words Look: Cross-Attention-Guided Patch Alignment with Contrastive and Transport Regularization for Bengali Captioning](https://mlsanity.com/paper/2509.18369/) (2025)
- [Experimenting with Self-Supervision using Rotation Prediction for Image Captioning](https://mlsanity.com/paper/2107.13111/) (2021)
- [KENGIC: KEyword-driven and N-Gram Graph based Image Captioning](https://mlsanity.com/paper/2302.03729/) (2023)

## Abstract (arXiv)

Image captioning is a multimodal task involving computer vision and natural language processing, where the goal is to learn a mapping from the image to its natural language description. In general, the mapping function is learned from a training set of image-caption pairs. However, for some language, large scale image-caption paired corpus might not be available. We present an approach to this unpaired image captioning problem by language pivoting. Our method can effectively capture the characteristics of an image captioner from the pivot language (Chinese) and align it to the target language (English) using another pivot-target (Chinese-English) sentence parallel corpus. We evaluate our method on two image-to-English benchmark datasets: MSCOCO and Flickr30K. Quantitative comparisons against several baseline approaches demonstrate the effectiveness of our method.