# DPC: Dual-Prompt Collaboration for Tuning Vision-Language Models

arXiv: https://arxiv.org/abs/2503.13443 · HTML: https://mlsanity.com/paper/2503.13443/

## Key facts

- Cited by 18 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 37 of its 52 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## 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 37)

- [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)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [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)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [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)
- [Natural Adversarial Examples](https://mlsanity.com/paper/1907.07174/) (2019)
- [CLIP-Adapter: Better Vision-Language Models with Feature Adapters](https://mlsanity.com/paper/2110.04544/) (2021)
- [Learning Robust Global Representations by Penalizing Local Predictive Power](https://mlsanity.com/paper/1905.13549/) (2019)
- [MaPLe: Multi-modal Prompt Learning](https://mlsanity.com/paper/2210.03117/) (2022)
- [Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey](https://mlsanity.com/paper/2403.14608/) (2024)
- [Prompt-aligned Gradient for Prompt Tuning](https://mlsanity.com/paper/2205.14865/) (2022)
- [Visual-Language Prompt Tuning with Knowledge-guided Context Optimization](https://mlsanity.com/paper/2303.13283/) (2023)
- [Self-regulating Prompts: Foundational Model Adaptation without Forgetting](https://mlsanity.com/paper/2307.06948/) (2023)
- [Fine-tuned CLIP Models are Efficient Video Learners](https://mlsanity.com/paper/2212.03640/) (2022)
- [Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners](https://mlsanity.com/paper/2303.02151/) (2023)
- [Unified Vision and Language Prompt Learning](https://mlsanity.com/paper/2210.07225/) (2022)
- [TCP:Textual-based Class-aware Prompt tuning for Visual-Language Model](https://mlsanity.com/paper/2311.18231/) (2023)
- [Consistency-guided Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2306.01195/) (2023)
- [PromptKD: Unsupervised Prompt Distillation for Vision-Language Models](https://mlsanity.com/paper/2403.02781/) (2024)
- [DePT: Decoupled Prompt Tuning](https://mlsanity.com/paper/2309.07439/) (2023)

## Built on by (most cited first, 18 of 18)

- [Generalizing vision-language models to novel domains: A comprehensive survey](https://mlsanity.com/paper/2506.18504/) (2025)
- [MAO: Efficient Model-Agnostic Optimization of Prompt Tuning for Vision-Language Models](https://mlsanity.com/paper/2503.18160/) (2025)
- [Raw Data Matters: Enhancing Prompt Tuning by Internal Augmentation on Vision-Language Models](https://mlsanity.com/paper/2508.02671/) (2025)
- [VaMP: Variational Multi-Modal Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2511.22664/) (2025)
- [AnchorOPT: Towards Optimizing Dynamic Anchors for Adaptive Prompt Learning](https://mlsanity.com/paper/2511.21188/) (2025)
- [Prompt Tuning for CLIP on the Pretrained Manifold](https://mlsanity.com/paper/2602.19198/) (2026)
- [FVG-PT: Adaptive Foreground View-Guided Prompt Tuning for Vision-Language Models](https://mlsanity.com/paper/2603.08708/) (2026)
- [GA2-CLIP: Generic Attribute Anchor for Efficient Prompt Tuningin Video-Language Models](https://mlsanity.com/paper/2511.22125/) (2025)
- [From Points to Clouds: Learning Robust Semantic Distributions for Multi-modal Prompts](https://mlsanity.com/paper/2511.22897/) (2025)
- [CAPT: Confusion-Aware Prompt Tuning for Reducing Vision-Language Misalignment](https://mlsanity.com/paper/2603.02557/) (2026)
- [Cluster-Aware Neural Collapse Prompt Tuning for Long-Tailed Generalization of Vision-Language Models](https://mlsanity.com/paper/2605.11939/) (2026)
- [Neutral-Reference Prompting for Vision-Language Models](https://mlsanity.com/paper/2605.15615/) (2026)
- [Evo-RAD: Navigating Rare Retinal Disease Diagnosis via Self-Evolving Agentic Retrieval](https://mlsanity.com/paper/2606.22955/) (2026)
- [MQAdapter: Multi-Modal Quantum Adapter for Coarse-to-Fine VLM Fine-tuning](https://mlsanity.com/paper/2607.12418/) (2026)
- [PPOM: Marginalizing Patch-Grid Phase for CLIP-Based Generalizable Vision-Language Prompt Tuning](https://mlsanity.com/paper/2608.13969/) (2026)
- [ProCAP: Probabilistic Cross-Attentive Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2609.30434/) (2026)
- [Diffusion Meta-Prompting and Steering for Generalizable Foundation Model Adaptation](https://mlsanity.com/paper/2610.11067/) (2026)
- [BLEND: Balancing Personalization vs. Generalization in Federated Vision-Language Models](https://mlsanity.com/paper/2610.12907/) (2026)

## Abstract (arXiv)

The Base-New Trade-off (BNT) problem universally exists during the optimization of CLIP-based prompt tuning, where continuous fine-tuning on base (target) classes leads to a simultaneous decrease of generalization ability on new (unseen) classes. Existing approaches attempt to regulate the prompt tuning process to balance BNT by appending constraints. However, imposed on the same target prompt, these constraints fail to fully avert the mutual exclusivity between the optimization directions for base and new. As a novel solution to this challenge, we propose the plug-and-play Dual-Prompt Collaboration (DPC) framework, the first that decoupling the optimization processes of base and new tasks at the prompt level. Specifically, we clone a learnable parallel prompt based on the backbone prompt, and introduce a variable Weighting-Decoupling framework to independently control the optimization directions of dual prompts specific to base or new tasks, thus avoiding the conflict in generalization. Meanwhile, we propose a Dynamic Hard Negative Optimizer, utilizing dual prompts to construct a more challenging optimization task on base classes for enhancement. For interpretability, we prove the feature channel invariance of the prompt vector during the optimization process, providing theoretical support for the Weighting-Decoupling of DPC. Extensive experiments on multiple backbones demonstrate that DPC can significantly improve base performance without introducing any external knowledge beyond the base classes, while maintaining generalization to new classes. Code is available at: https://github.com/JREion/DPC.