# Consistency-guided Prompt Learning for Vision-Language Models

arXiv: https://arxiv.org/abs/2306.01195 · HTML: https://mlsanity.com/paper/2306.01195/

## Key facts

- Cited by 73 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 30 of its 41 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## 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 30)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [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)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (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)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [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)
- [Florence: A New Foundation Model for Computer Vision](https://mlsanity.com/paper/2111.11432/) (2021)
- [EVA: Exploring the Limits of Masked Visual Representation Learning at Scale](https://mlsanity.com/paper/2211.07636/) (2022)
- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021)
- [MaPLe: Multi-modal Prompt Learning](https://mlsanity.com/paper/2210.03117/) (2022)
- [FILIP: Fine-grained Interactive Language-Image Pre-Training](https://mlsanity.com/paper/2111.07783/) (2021)
- [LiT: Zero-Shot Transfer with Locked-image text Tuning](https://mlsanity.com/paper/2111.07991/) (2021)
- [Vision Transformer Adapter for Dense Predictions](https://mlsanity.com/paper/2205.08534/) (2022)
- [DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting](https://mlsanity.com/paper/2112.01518/) (2021)
- [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)
- [Exploring Visual Prompts for Adapting Large-Scale Models](https://mlsanity.com/paper/2203.17274/) (2022)
- [Zero-Shot Learning -- The Good, the Bad and the Ugly](https://mlsanity.com/paper/1703.04394/) (2017)
- [Prompt Distribution Learning](https://mlsanity.com/paper/2205.03340/) (2022)
- [Bayesian Prompt Learning for Image-Language Model Generalization](https://mlsanity.com/paper/2210.02390/) (2022)

## Built on by (most cited first, 30 of 73)

- [Advancing Textual Prompt Learning with Anchored Attributes](https://mlsanity.com/paper/2412.09442/) (2024)
- [DPC: Dual-Prompt Collaboration for Tuning Vision-Language Models](https://mlsanity.com/paper/2503.13443/) (2025)
- [ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models](https://mlsanity.com/paper/2501.11175/) (2025)
- [PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization](https://mlsanity.com/paper/2409.17137/) (2024)
- [IPO: Interpretable Prompt Optimization for Vision-Language Models](https://mlsanity.com/paper/2410.15397/) (2024)
- [CustomContrast: A Multilevel Contrastive Perspective For Subject-Driven Text-to-Image Customization](https://mlsanity.com/paper/2409.05606/) (2024)
- [OpenworldAUC: Towards Unified Evaluation and Optimization for Open-world Prompt Tuning](https://mlsanity.com/paper/2505.05180/) (2025)
- [Hierarchical Cross-modal Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2507.14976/) (2025)
- [Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves](https://mlsanity.com/paper/2412.11509/) (2024)
- [Mettle: Meta-Token Learning for Memory-Efficient Audio-Visual Adaptation](https://mlsanity.com/paper/2506.23271/) (2025)
- [Understanding and Mitigating Miscalibration in Prompt Tuning for Vision-Language Models](https://mlsanity.com/paper/2410.02681/) (2024)
- [Rethinking Misalignment in Vision-Language Model Adaptation from a Causal Perspective](https://mlsanity.com/paper/2410.12816/) (2024)
- [Conceptual Codebook Learning for Vision-Language Models](https://mlsanity.com/paper/2407.02350/) (2024)
- [Tree of Attributes Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2410.11201/) (2024)
- [TransAgent: Transfer Vision-Language Foundation Models with Heterogeneous Agent Collaboration](https://mlsanity.com/paper/2410.12183/) (2024)
- [Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models](https://mlsanity.com/paper/2411.16018/) (2024)
- [Medical Knowledge Intervention Prompt Tuning for Medical Image Classification](https://mlsanity.com/paper/2511.12639/) (2025)
- [MMLoP: Multi-Modal Low-Rank Prompting for Efficient Vision-Language Adaptation](https://mlsanity.com/paper/2602.21397/) (2026)
- [Efficient Transfer Learning for Video-language Foundation Models](https://mlsanity.com/paper/2411.11223/) (2024)
- [Prompt-OT: An Optimal Transport Regularization Paradigm for Knowledge Preservation in Vision-Language Model Adaptation](https://mlsanity.com/paper/2503.08906/) (2025)
- [FedMVP: Federated Multimodal Visual Prompt Tuning for Vision-Language Models](https://mlsanity.com/paper/2504.20860/) (2025)
- [MMRL++: Parameter-Efficient and Interaction-Aware Representation Learning for Vision-Language Models](https://mlsanity.com/paper/2505.10088/) (2025)
- [Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting](https://mlsanity.com/paper/2507.20834/) (2025)
- [Elevating All Zero-Shot Sketch-Based Image Retrieval Through Multimodal Prompt Learning](https://mlsanity.com/paper/2407.04207/) (2024)
- [Learning Generalizable Prompt for CLIP with Class Similarity Knowledge](https://mlsanity.com/paper/2502.11969/) (2025)
- [Generalizable Prompt Learning of CLIP: A Brief Overview](https://mlsanity.com/paper/2503.01263/) (2025)
- [MAO: Efficient Model-Agnostic Optimization of Prompt Tuning for Vision-Language Models](https://mlsanity.com/paper/2503.18160/) (2025)
- [Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detection](https://mlsanity.com/paper/2504.11055/) (2025)
- [GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection](https://mlsanity.com/paper/2504.14919/) (2025)
- [Diff-Prompt: Diffusion-Driven Prompt Generator with Mask Supervision](https://mlsanity.com/paper/2504.21423/) (2025)

## Abstract (arXiv)

We propose Consistency-guided Prompt learning (CoPrompt), a new fine-tuning method for vision-language models. Our approach improves the generalization of large foundation models when fine-tuned on downstream tasks in a few-shot setting. The basic idea of CoPrompt is to enforce a consistency constraint in the prediction of the trainable and pre-trained models to prevent overfitting on the downstream task. Additionally, we introduce the following two components into our consistency constraint to further boost the performance: enforcing consistency on two perturbed inputs and combining two dominant paradigms of tuning, prompting and adapter. Enforcing consistency on perturbed input serves to further regularize the consistency constraint, thereby improving generalization. Moreover, the integration of adapters and prompts not only enhances performance on downstream tasks but also offers increased tuning flexibility in both input and output spaces. This facilitates more effective adaptation to downstream tasks in a few-shot learning setting. Experiments show that CoPrompt outperforms existing methods on a range of evaluation suites, including base-to-novel generalization, domain generalization, and cross-dataset evaluation. On generalization, CoPrompt improves the state-of-the-art on zero-shot tasks and the overall harmonic mean over 11 datasets. Detailed ablation studies show the effectiveness of each of the components in CoPrompt. We make our code available at https://github.com/ShuvenduRoy/CoPrompt.