mlsanityData to 14 Oct 2026

ChordPrompt: Orchestrating Cross-Modal Prompt Synergy for Multi-Domain Incremental Learning in CLIP

arXiv 2506.19608 ↗First posted 24 Jun 2025cs.AI cs.LGcited by 0

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

Abstract (arXiv)

Continual learning (CL) empowers pre-trained vision-language models to adapt effectively to novel or previously underrepresented data distributions without comprehensive retraining, enhancing their adaptability and efficiency. While vision-language models like CLIP show great promise, they struggle to maintain performance across domains in incremental learning scenarios. Existing prompt learning methods face two main limitations: 1) they primarily focus on class-incremental learning scenarios, lacking specific strategies for multi-domain task incremental learning; 2) most current approaches employ single-modal prompts, neglecting the potential benefits of cross-modal information exchange. To address these challenges, we propose the ChordPrompt framework, which facilitates a harmonious interplay between visual and textual prompts. ChordPrompt introduces cross-modal prompts to leverage interactions between visual and textual information. Our approach also employs domain-adaptive text prompts to select appropriate prompts for continual adaptation across multiple domains. Comprehensive experiments on multi-domain incremental learning benchmarks demonstrate that ChordPrompt outperforms state-of-the-art methods in zero-shot generalization and downstream task performance.

Key facts. No citing papers in the map yet (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 33 of its 39 references that are in the map. First posted on arXiv 24 Jun 2025.

Builds on

33 of 39 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters18 Mar
  2. 2023Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models12 Mar
  3. 2022Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model17 Aug
  4. 2022Conditional Prompt Learning for Vision-Language Models10 Mar
  5. 2021Learning to Prompt for Vision-Language Models2 Sep

Other references · 28

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2023Visual Instruction Tuningcited by 7,869
  4. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  5. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,137
  6. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,386
  7. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  8. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,864
  9. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  10. 2016Learning without Forgettingcited by 2,496
  11. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,333
  12. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,222
16 more
  1. 2020The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalizationcited by 1,850
  2. 2016Progressive Neural Networkscited by 1,361
  3. 2017Continual Learning with Deep Generative Replaycited by 1,158
  4. 2021Learning to Prompt for Continual Learningcited by 944
  5. 2021Robust fine-tuning of zero-shot modelscited by 797
  6. 2017Lifelong Learning with Dynamically Expandable Networkscited by 694
  7. 2022MaPLe: Multi-modal Prompt Learningcited by 687
  8. 2022DualPrompt: Complementary Prompting for Rehearsal-free Continual Learningcited by 642
  9. 2019Three scenarios for continual learningcited by 614
  10. 2021DyTox: Transformers for Continual Learning with DYnamic TOken eXpansioncited by 291
  11. 2022S-Prompts Learning with Pre-trained Transformers: An Occam's Razor for Domain Incremental Learningcited by 246
  12. 2023Visual Prompt Multi-Modal Trackingcited by 142
  13. 2022CLIP model is an Efficient Continual Learnercited by 65
  14. 2022Don't Stop Learning: Towards Continual Learning for the CLIP Modelcited by 61
  15. 2023Continual Vision-Language Representation Learning with Off-Diagonal Informationcited by 40
  16. 2023BiRT: Bio-inspired Replay in Vision Transformers for Continual Learningcited by 25

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  1. 2026CMAP: Cross-Modal Adaptive Prompting for Multi-Domain Task-Incremental Learning
  2. 2025Preserving Cross-Modal Consistency for CLIP-based Class-Incremental Learning
  3. 2024MuAP: Multi-step Adaptive Prompt Learning for Vision-Language Model with Missing Modality
  4. 2022Learning Domain Invariant Prompt for Vision-Language Models
  5. 2026ProCAP: Probabilistic Cross-Attentive Prompt Learning for Vision-Language Models
  6. 2024CLIP with Generative Latent Replay: a Strong Baseline for Incremental Learning
  7. 2025Harnessing Textual Semantic Priors for Knowledge Transfer and Refinement in CLIP-Driven Continual Learning
  8. 2026Understanding Cross-Modal Contributions in Continual Vision-Language Models: A Theoretical Perspective