# Prompt-based Learning for Unpaired Image Captioning

arXiv: https://arxiv.org/abs/2205.13125 · HTML: https://mlsanity.com/paper/2205.13125/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 59 of its 87 references that are in the map.
- First posted on arXiv 26 May 2022.

## Builds on (most cited first, 30 of 59)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Learning to Prompt for Continual Learning](https://mlsanity.com/paper/2112.08654/) (2021)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [VizWiz Grand Challenge: Answering Visual Questions from Blind People](https://mlsanity.com/paper/1802.08218/) (2018)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016)
- [Image Transformer](https://mlsanity.com/paper/1802.05751/) (2018)
- [Learning To Retrieve Prompts for In-Context Learning](https://mlsanity.com/paper/2112.08633/) (2021)
- [PointCLIP: Point Cloud Understanding by CLIP](https://mlsanity.com/paper/2112.02413/) (2021)
- [Visual Dialog](https://mlsanity.com/paper/1611.08669/) (2016)
- [CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields](https://mlsanity.com/paper/2112.05139/) (2021)
- [Adversarial Feature Learning](https://mlsanity.com/paper/1605.09782/) (2016)
- [Meshed-Memory Transformer for Image Captioning](https://mlsanity.com/paper/1912.08226/) (2019)
- [How Much Can CLIP Benefit Vision-and-Language Tasks?](https://mlsanity.com/paper/2107.06383/) (2021)
- [A Recurrent Vision-and-Language BERT for Navigation](https://mlsanity.com/paper/2011.13922/) (2020)
- [Attention on Attention for Image Captioning](https://mlsanity.com/paper/1908.06954/) (2019)
- [Image Captioning with Semantic Attention](https://mlsanity.com/paper/1603.03925/) (2016)

## Built on by (most cited first, 23 of 23)

- [Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey](https://mlsanity.com/paper/2302.10035/) (2023)
- [AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2306.04717/) (2023)
- [Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?](https://mlsanity.com/paper/2401.12902/) (2024)
- [Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion](https://mlsanity.com/paper/2312.10692/) (2023)
- [Language-free Training for Zero-shot Video Grounding](https://mlsanity.com/paper/2210.12977/) (2022)
- [Improving Cross-modal Alignment with Synthetic Pairs for Text-only Image Captioning](https://mlsanity.com/paper/2312.08865/) (2023)
- [CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention](https://mlsanity.com/paper/2506.11073/) (2025)
- [CgT-GAN: CLIP-guided Text GAN for Image Captioning](https://mlsanity.com/paper/2308.12045/) (2023)
- [Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification](https://mlsanity.com/paper/2306.02243/) (2023)
- [Evolution-based Region Adversarial Prompt Learning for Robustness Enhancement in Vision-Language Models](https://mlsanity.com/paper/2503.12874/) (2025)
- [MoPE: Mixture of Prompt Experts for Parameter-Efficient and Scalable Multimodal Fusion](https://mlsanity.com/paper/2403.10568/) (2024)
- [Semi-Supervised Image Captioning by Adversarially Propagating Labeled Data](https://mlsanity.com/paper/2301.11174/) (2023)
- [Counterfactual Co-occurring Learning for Bias Mitigation in Weakly-supervised Object Localization](https://mlsanity.com/paper/2305.15354/) (2023)
- [Mitigating Multilingual Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2408.00550/) (2024)
- [M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios](https://mlsanity.com/paper/2303.05122/) (2023)
- [Enhancing Vision-Language Models Generalization via Diversity-Driven Novel Feature Synthesis](https://mlsanity.com/paper/2405.02586/) (2024)
- [MoPD: Mixture-of-Prompts Distillation for Vision-Language Models](https://mlsanity.com/paper/2412.19087/) (2024)
- [Adaptively Clustering Neighbor Elements for Image-Text Generation](https://mlsanity.com/paper/2301.01955/) (2023)
- [Exploring Annotation-free Image Captioning with Retrieval-augmented Pseudo Sentence Generation](https://mlsanity.com/paper/2307.14750/) (2023)
- [Multimodal Graph Network Modeling for Human-Object Interaction Detection with PDE Graph Diffusion](https://mlsanity.com/paper/2509.12554/) (2025)
- [PC-MNet: Dual-Level Congruity Modeling for Multimodal Sarcasm Detection via Polarity-Modulated Attention](https://mlsanity.com/paper/2605.02447/) (2026)
- [Event Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithms](https://mlsanity.com/paper/2607.09114/) (2026)
- [Generative Retrieval for Unsupervised Text-Based Person Search](https://mlsanity.com/paper/2609.12965/) (2026)

## Abstract (arXiv)

Unpaired Image Captioning (UIC) has been developed to learn image descriptions from unaligned vision-language sample pairs. Existing works usually tackle this task using adversarial learning and visual concept reward based on reinforcement learning. However, these existing works were only able to learn limited cross-domain information in vision and language domains, which restrains the captioning performance of UIC. Inspired by the success of Vision-Language Pre-Trained Models (VL-PTMs) in this research, we attempt to infer the cross-domain cue information about a given image from the large VL-PTMs for the UIC task. This research is also motivated by recent successes of prompt learning in many downstream multi-modal tasks, including image-text retrieval and vision question answering. In this work, a semantic prompt is introduced and aggregated with visual features for more accurate caption prediction under the adversarial learning framework. In addition, a metric prompt is designed to select high-quality pseudo image-caption samples obtained from the basic captioning model and refine the model in an iterative manner. Extensive experiments on the COCO and Flickr30K datasets validate the promising captioning ability of the proposed model. We expect that the proposed prompt-based UIC model will stimulate a new line of research for the VL-PTMs based captioning.