# Learning Domain Invariant Prompt for Vision-Language Models

arXiv: https://arxiv.org/abs/2212.04196 · HTML: https://mlsanity.com/paper/2212.04196/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Placed in Prompt learning and adaptation of CLIP, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 57 of its 96 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Placed in

- Multimodal LLMs / vision-language models / [Prompt learning and adaptation of CLIP](https://mlsanity.com/mllm/prompt-learning-and-adaptation-of-clip/)

## Builds on (most cited first, 30 of 57)

- [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)
- [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)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [In Search of Lost Domain Generalization](https://mlsanity.com/paper/2007.01434/) (2020)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Deeper, Broader and Artier Domain Generalization](https://mlsanity.com/paper/1710.03077/) (2017)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021)

## Built on by (most cited first, 30 of 36)

- [Visual Tuning](https://mlsanity.com/paper/2305.06061/) (2023)
- [RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation](https://mlsanity.com/paper/2307.00997/) (2023)
- [MSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuning](https://mlsanity.com/paper/2408.11505/) (2024)
- [Enhancing Perception of Key Changes in Remote Sensing Image Change Captioning](https://mlsanity.com/paper/2409.12612/) (2024)
- [Explore with Long-term Memory: A Benchmark and Multimodal LLM-based Reinforcement Learning Framework for Embodied Exploration](https://mlsanity.com/paper/2601.10744/) (2026)
- [Hierarchical Cross-modal Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2507.14976/) (2025)
- [IAP: Improving Continual Learning of Vision-Language Models via Instance-Aware Prompting](https://mlsanity.com/paper/2503.20612/) (2025)
- [Adversarial Prompt Distillation for Vision-Language Models](https://mlsanity.com/paper/2411.15244/) (2024)
- [CKAA: Cross-subspace Knowledge Alignment and Aggregation for Robust Continual Learning](https://mlsanity.com/paper/2507.09471/) (2025)
- [MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation](https://mlsanity.com/paper/2602.21397/) (2026)
- [Multi-Stage Knowledge Integration of Vision-Language Models for Continual Learning](https://mlsanity.com/paper/2411.06764/) (2024)
- [MMRL++: Parameter-Efficient and Interaction-Aware Representation Learning for Vision-Language Models](https://mlsanity.com/paper/2505.10088/) (2025)
- [UPRE: Zero-Shot Domain Adaptation for Object Detection via Unified Prompt and Representation Enhancement](https://mlsanity.com/paper/2507.00721/) (2025)
- [Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization](https://mlsanity.com/paper/2606.21736/) (2026)
- [SETA: Semantic-Aware Token Augmentation for Domain Generalization](https://mlsanity.com/paper/2403.11792/) (2024)
- [Empowering Source-Free Domain Adaptation via MLLM-Guided Reliability-Based Curriculum Learning](https://mlsanity.com/paper/2405.18376/) (2024)
- [MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI Classification](https://mlsanity.com/paper/2502.07409/) (2025)
- [ActPrompt: In-Domain Feature Adaptation via Action Cues for Video Temporal Grounding](https://mlsanity.com/paper/2408.06622/) (2024)
- [HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling](https://mlsanity.com/paper/2408.14812/) (2024)
- [Exploring Interpretability for Visual Prompt Tuning with Cross-layer Concepts](https://mlsanity.com/paper/2503.06084/) (2025)
- [Can Out-of-Domain data help to Learn Domain-Specific Prompts for Multimodal Misinformation Detection?](https://mlsanity.com/paper/2311.16496/) (2023)
- [MoPD: Mixture-of-Prompts Distillation for Vision-Language Models](https://mlsanity.com/paper/2412.19087/) (2024)
- [Simple yet Effective Semi-supervised Knowledge Distillation from Vision-Language Models via Dual-Head Optimization](https://mlsanity.com/paper/2505.07675/) (2025)
- [Alternating Training-based Label Smoothing Enhances Prompt Generalization](https://mlsanity.com/paper/2508.17846/) (2025)
- [Hierarchical Multi-modal Transformer for Cross-modal Long Document Classification](https://mlsanity.com/paper/2407.10105/) (2024)
- [PCoreSet: Effective Active Learning through Knowledge Distillation from Vision-Language Models](https://mlsanity.com/paper/2506.00910/) (2025)
- [Integrated Structural Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2507.05677/) (2025)
- [Remodeling Semantic Relationships in Vision-Language Fine-Tuning](https://mlsanity.com/paper/2511.08238/) (2025)
- [Automatic Attack Discovery for Few-Shot Class-Incremental Learning via Large Language Models](https://mlsanity.com/paper/2512.03882/) (2025)
- [Fourier-Attentive Representation Learning: A Fourier-Guided Framework for Few-Shot Generalization in Vision-Language Models](https://mlsanity.com/paper/2512.04395/) (2025)

## Abstract (arXiv)

Prompt learning is one of the most effective and trending ways to adapt powerful vision-language foundation models like CLIP to downstream datasets by tuning learnable prompt vectors with very few samples. However, although prompt learning achieves excellent performance over in-domain data, it still faces the major challenge of generalizing to unseen classes and domains. Some existing prompt learning methods tackle this issue by adaptively generating different prompts for different tokens or domains but neglecting the ability of learned prompts to generalize to unseen domains. In this paper, we propose a novel prompt learning paradigm that directly generates domain invariant prompt that can be generalized to unseen domains, called MetaPrompt. Specifically, a dual-modality prompt tuning network is proposed to generate prompts for input from both image and text modalities. With a novel asymmetric contrastive loss, the representation from the original pre-trained vision-language model acts as supervision to enhance the generalization ability of the learned prompt. More importantly, we propose a meta-learning-based prompt tuning algorithm that explicitly constrains the task-specific prompt tuned for one domain or class to also achieve good performance in another domain or class. Extensive experiments on 11 datasets for base-to-new generalization and 4 datasets for domain generalization demonstrate that our method consistently and significantly outperforms existing methods.