# Multimodal Prompting with Missing Modalities for Visual Recognition

arXiv: https://arxiv.org/abs/2303.03369 · HTML: https://mlsanity.com/paper/2303.03369/

## Key facts

- Cited by 121 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 48 references that are in the map.
- First posted on arXiv 6 Mar 2023.

## Builds on (most cited first, 30 of 39)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [Scaling Language Models: Methods, Analysis & Insights from Training Gopher](https://mlsanity.com/paper/2112.11446/) (2021)
- [Learning to Prompt for Continual Learning](https://mlsanity.com/paper/2112.08654/) (2021)
- [MaPLe: Multi-modal Prompt Learning](https://mlsanity.com/paper/2210.03117/) (2022)
- [Do Vision Transformers See Like Convolutional Neural Networks?](https://mlsanity.com/paper/2108.08810/) (2021)
- [Multimodal Few-Shot Learning with Frozen Language Models](https://mlsanity.com/paper/2106.13884/) (2021)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning](https://mlsanity.com/paper/2204.04799/) (2022)
- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [Multi-modal Transformer for Video Retrieval](https://mlsanity.com/paper/2007.10639/) (2020)
- [On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines](https://mlsanity.com/paper/2006.04884/) (2020)
- [Gated Multimodal Units for Information Fusion](https://mlsanity.com/paper/1702.01992/) (2017)
- [SMIL: Multimodal Learning with Severely Missing Modality](https://mlsanity.com/paper/2103.05677/) (2021)
- [Are Multimodal Transformers Robust to Missing Modality?](https://mlsanity.com/paper/2204.05454/) (2022)
- [Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval](https://mlsanity.com/paper/2302.03084/) (2023)
- [Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities](https://mlsanity.com/paper/1812.07809/) (2018)
- [End-to-End Referring Video Object Segmentation with Multimodal Transformers](https://mlsanity.com/paper/2111.14821/) (2021)
- [What Language Model to Train if You Have One Million GPU Hours?](https://mlsanity.com/paper/2210.15424/) (2022)
- [FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction](https://mlsanity.com/paper/2203.08411/) (2022)

## Built on by (most cited first, 30 of 121)

- [Deep Multimodal Learning with Missing Modality: A Survey](https://mlsanity.com/paper/2409.07825/) (2024)
- [Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions](https://mlsanity.com/paper/2404.03264/) (2024)
- [Single-Model and Any-Modality for Video Object Tracking](https://mlsanity.com/paper/2311.15851/) (2023)
- [Robust Multimodal Learning with Missing Modalities via Parameter-Efficient Adaptation](https://mlsanity.com/paper/2310.03986/) (2023)
- [Multimodal Prompt Learning with Missing Modalities for Sentiment Analysis and Emotion Recognition](https://mlsanity.com/paper/2407.05374/) (2024)
- [SimMLM: A Simple Framework for Multi-modal Learning with Missing Modality](https://mlsanity.com/paper/2507.19264/) (2025)
- [Prompt-based Adaptation in Large-scale Vision Models: A Survey](https://mlsanity.com/paper/2510.13219/) (2025)
- [Missing Modality Prediction for Unpaired Multimodal Learning via Joint Embedding of Unimodal Models](https://mlsanity.com/paper/2407.12616/) (2024)
- [All in One Framework for Multimodal Re-identification in the Wild](https://mlsanity.com/paper/2405.04741/) (2024)
- [Deep Correlated Prompting for Visual Recognition with Missing Modalities](https://mlsanity.com/paper/2410.06558/) (2024)
- [Modality-missing RGBT Tracking: Invertible Prompt Learning and High-quality Benchmarks](https://mlsanity.com/paper/2312.16244/) (2023)
- [Towards Robust Multimodal Prompting With Missing Modalities](https://mlsanity.com/paper/2312.15890/) (2023)
- [Quantifying and Enhancing Multi-modal Robustness with Modality Preference](https://mlsanity.com/paper/2402.06244/) (2024)
- [Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learning](https://mlsanity.com/paper/2501.01120/) (2025)
- [Delving into Multimodal Prompting for Fine-grained Visual Classification](https://mlsanity.com/paper/2309.08912/) (2023)
- [A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios](https://mlsanity.com/paper/2409.17864/) (2024)
- [Robust Multimodal Survival Prediction with the Latent Differentiation Conditional Variational AutoEncoder](https://mlsanity.com/paper/2503.09496/) (2025)
- [Fourier Prompt Tuning for Modality-Incomplete Scene Segmentation](https://mlsanity.com/paper/2401.16923/) (2024)
- [Distilled Prompt Learning for Incomplete Multimodal Survival Prediction](https://mlsanity.com/paper/2503.01653/) (2025)
- [MuAP: Multi-step Adaptive Prompt Learning for Vision-Language Model with Missing Modality](https://mlsanity.com/paper/2409.04693/) (2024)
- [MMP: Towards Robust Multi-Modal Learning with Masked Modality Projection](https://mlsanity.com/paper/2410.03010/) (2024)
- [Visual Prompt Flexible-Modal Face Anti-Spoofing](https://mlsanity.com/paper/2307.13958/) (2023)
- [Unified-modal Salient Object Detection via Adaptive Prompt Learning](https://mlsanity.com/paper/2311.16835/) (2023)
- [Recurrent Aligned Network for Generalized Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2403.05810/) (2024)
- [Knowledge Bridger: Towards Training-free Missing Modality Completion](https://mlsanity.com/paper/2502.19834/) (2025)
- [See-Saw Modality Balance: See Gradient, and Sew Impaired Vision-Language Balance to Mitigate Dominant Modality Bias](https://mlsanity.com/paper/2503.13834/) (2025)
- [Reducing Unimodal Bias in Multi-Modal Semantic Segmentation with Multi-Scale Functional Entropy Regularization](https://mlsanity.com/paper/2505.06635/) (2025)
- [ChatGPT-Like Large-Scale Foundation Models for Prognostics and Health Management: A Survey and Roadmaps](https://mlsanity.com/paper/2305.06472/) (2023)
- [Efficient Prompting for Continual Adaptation to Missing Modalities](https://mlsanity.com/paper/2503.00528/) (2025)
- [ReID5o: Achieving Omni Multi-modal Person Re-identification in a Single Model](https://mlsanity.com/paper/2506.09385/) (2025)

## Abstract (arXiv)

In this paper, we tackle two challenges in multimodal learning for visual recognition: 1) when missing-modality occurs either during training or testing in real-world situations; and 2) when the computation resources are not available to finetune on heavy transformer models. To this end, we propose to utilize prompt learning and mitigate the above two challenges together. Specifically, our modality-missing-aware prompts can be plugged into multimodal transformers to handle general missing-modality cases, while only requiring less than 1% learnable parameters compared to training the entire model. We further explore the effect of different prompt configurations and analyze the robustness to missing modality. Extensive experiments are conducted to show the effectiveness of our prompt learning framework that improves the performance under various missing-modality cases, while alleviating the requirement of heavy model re-training. Code is available.