mlsanityData to 7 Oct 2026

Transitive Vision-Language Prompt Learning for Domain Generalization

arXiv 2404.18758 ↗First posted 29 Apr 2024cs.CV cs.LGcited by 2

On the map: Multimodal LLMs / vision-language models → Prompt learning and adaptation of CLIP

Abstract (arXiv)

The vision-language pre-training has enabled deep models to make a huge step forward in generalizing across unseen domains. The recent learning method based on the vision-language pre-training model is a great tool for domain generalization and can solve this problem to a large extent. However, there are still some issues that an advancement still suffers from trading-off between domain invariance and class separability, which are crucial in current DG problems. However, there are still some issues that an advancement still suffers from trading-off between domain invariance and class separability, which are crucial in current DG problems. In this paper, we introduce a novel prompt learning strategy that leverages deep vision prompts to address domain invariance while utilizing language prompts to ensure class separability, coupled with adaptive weighting mechanisms to balance domain invariance and class separability. Extensive experiments demonstrate that deep vision prompts effectively extract domain-invariant features, significantly improving the generalization ability of deep models and achieving state-of-the-art performance on three datasets.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Prompt learning and adaptation of CLIP, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 47 of its 61 references that are in the map. First posted on arXiv 29 Apr 2024.

Builds on

47 of 61 references.

Closest ancestors · 7

Same direction, newest first

  1. 2023Robust Fine-Tuning of Vision-Language Models for Domain Generalization3 Nov
  2. 2023A Sentence Speaks a Thousand Images: Domain Generalization through Distilling CLIP with Language Guidance21 Sep
  3. 2022Prompt-aligned Gradient for Prompt Tuning30 May
  4. 2022Conditional Prompt Learning for Vision-Language Models10 Mar
  5. 2021Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling6 Nov
  6. 2021CLIP-Adapter: Better Vision-Language Models with Feature Adapters9 Oct
  7. 2021Learning to Prompt for Vision-Language Models2 Sep

Other references · 40

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  4. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  5. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  6. 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
  7. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,219
  8. 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,548
  9. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,327
  10. 2016Deep CORAL: Correlation Alignment for Deep Domain Adaptationcited by 1,194
  11. 2017Deep Hashing Network for Unsupervised Domain Adaptationcited by 1,181
  12. 2020In Search of Lost Domain Generalizationcited by 1,042
28 more
  1. 2017Deeper, Broader and Artier Domain Generalizationcited by 983
  2. 2019Decoupling Representation and Classifier for Long-Tailed Recognitioncited by 806
  3. 2022Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distributioncited by 722
  4. 2022MaPLe: Multi-modal Prompt Learningcited by 685
  5. 2019BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognitioncited by 403
  6. 2023A Comprehensive Survey on Test-Time Adaptation under Distribution Shiftscited by 353
  7. 2023Self-regulating Prompts: Foundational Model Adaptation without Forgettingcited by 272
  8. 2020Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformationcited by 183
  9. 2017Domain Generalization by Marginal Transfer Learningcited by 180
  10. 2022VLP: A Survey on Vision-Language Pre-trainingcited by 159
  11. 2022Domain Generalization by Mutual-Information Regularization with Pre-trained Modelscited by 133
  12. 2019Feature-Critic Networks for Heterogeneous Domain Generalizationcited by 130
  13. 2023Sharpness-Aware Gradient Matching for Domain Generalizationcited by 102
  14. 2023CLIPood: Generalizing CLIP to Out-of-Distributionscited by 96
  15. 2019Support and Invertibility in Domain-Invariant Representationscited by 87
  16. 2021Exploiting Domain-Specific Features to Enhance Domain Generalizationcited by 86
  17. 2020Towards Universal Representation Learning for Deep Face Recognitioncited by 78
  18. 2022LPT: Long-tailed Prompt Tuning for Image Classificationcited by 69
  19. 2018Domain Generalization with Domain-Specific Aggregation Modulescited by 61
  20. 2022Prompt Vision Transformer for Domain Generalizationcited by 52
  21. 2023AttriCLIP: A Non-Incremental Learner for Incremental Knowledge Learningcited by 46
  22. 2023Meta-causal Learning for Single Domain Generalizationcited by 43
  23. 2021Domain Prompt Learning for Efficiently Adapting CLIP to Unseen Domainscited by 39
  24. 2022Domain-invariant Feature Exploration for Domain Generalizationcited by 34
  25. 2022On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyondcited by 26
  26. 2022Intra-Source Style Augmentation for Improved Domain Generalizationcited by 17
  27. 2023Adversarial Style Augmentation for Domain Generalizationcited by 16
  28. 2023Domain Generalization via Rationale Invariancecited by 16

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Modelscited by 23
  2. 2025Approximate Domain Unlearning for Vision-Language Modelscited by 6

Similar, not cited

Close in wording, no citation either way

  1. 2025Latent Domain Prompt Learning for Vision-Language Models
  2. 2024Soft Prompt Generation for Domain Generalization
  3. 2022Learning Domain Invariant Prompt for Vision-Language Models
  4. 2025Dual-Path Stable Soft Prompt Generation for Domain Generalization
  5. 2022Domain-Unified Prompt Representations for Source-Free Domain Generalization
  6. 2025CoDoL: Conditional Domain Prompt Learning for Out-of-Distribution Generalization
  7. 2024PromptSync: Bridging Domain Gaps in Vision-Language Models through Class-Aware Prototype Alignment and Discrimination
  8. 2025Prompt Disentanglement via Language Guidance and Representation Alignment for Domain Generalization