mlsanityData to 7 Oct 2026

Domain-Invariant Prompt Learning for Vision-Language Models

arXiv 2603.28555 ↗First posted 30 Mar 2026cs.CV cs.AIcited by 0

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

Abstract (arXiv)

Large pre-trained vision-language models like CLIP have transformed computer vision by aligning images and text in a shared feature space, enabling robust zero-shot transfer via prompting. Soft-prompting, such as Context Optimization (CoOp), effectively adapts these models for downstream recognition tasks by learning a set of context vectors. However, CoOp lacks explicit mechanisms for handling domain shifts across unseen distributions. To address this, we propose Domain-invariant Context Optimization (DiCoOp), an extension of CoOp optimized for domain generalization. By employing an adversarial training approach, DiCoOp forces the model to learn domain-invariant prompts while preserving discriminative power for classification. Experimental results show that DiCoOp consistently surpasses CoOp in domain generalization tasks across diverse visual domains.

Key facts. No citing papers in the map yet (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 18 of its 22 references that are in the map. First posted on arXiv 30 Mar 2026.

Builds on

18 of 22 references.

Closest ancestors · 3

Same direction, newest first

  1. 2022Conditional Prompt Learning for Vision-Language Models10 Mar
  2. 2021CLIP-Adapter: Better Vision-Language Models with Feature Adapters9 Oct
  3. 2021Learning to Prompt for Vision-Language Models2 Sep

Other references · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,848
  5. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  6. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  7. 2019VisualBERT: A Simple and Performant Baseline for Vision and Languagecited by 1,291
  8. 2017Deeper, Broader and Artier Domain Generalizationcited by 983
  9. 2021Domain Generalization: A Surveycited by 760
  10. 2022Domain Adaptation via Prompt Learningcited by 128
  11. 2023Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorldcited by 65
  12. 2023AD-CLIP: Adapting Domains in Prompt Space Using CLIPcited by 62
3 more
  1. 2023Source-Free Domain Adaptation with Frozen Multimodal Foundation Modelcited by 54
  2. 2024Domain Generalization through Meta-Learning: A Surveycited by 28
  3. 2024Rethinking Domain Adaptation and Generalization in the Era of CLIPcited by 5

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  1. 2022Learning Domain Invariant Prompt for Vision-Language Models
  2. 2024Soft Prompt Generation for Domain Generalization
  3. 2024Transitive Vision-Language Prompt Learning for Domain Generalization
  4. 2025Prompt Disentanglement via Language Guidance and Representation Alignment for Domain Generalization
  5. 2024Domain-Agnostic Mutual Prompting for Unsupervised Domain Adaptation
  6. 2025Latent Domain Prompt Learning for Vision-Language Models
  7. 2024Enhancing Domain Adaptation through Prompt Gradient Alignment
  8. 2023PRE: Vision-Language Prompt Learning with Reparameterization Encoder