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A curated list of awesome prompt/adapter learning methods for vision-language models like CLIP.
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| 2026-07-30 | 786 |
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# Awesome-Prompt-Adapter-Learning-for-VLMs
A curated list of prompt/adapter learning methods for vision-language models (e.g., CLIP, ALIGN).
# Table of Contents
- [Papers](#papers)
- [Surveys](#surveys)
- [Foundation Models](#foundation-models)
- [Datasets](#datasets)
- [General Prompt Learning](#general-prompt-learning)
- [Another form of Prompt](#another-form-of-prompt)
- [General Test-time Prompt Learning](#general-test-time-prompt-learning)
- [General Adapter Learning](#general-adapter-learning)
- [Video Understanding](#video-understanding)
- [Continual Learning](#continual-learning)
- [Others](#others)
## 💡Tips:
- If you know that some papers published in top conferences (CVPR, ICCV, ECCV, ICML, NeurlPS, ICLR) or journals (TPAMI, IJCV, TIP) have not been included in this list, please feel free to contact me at any time, either by sending an email (zhengli97[at]qq.com) or submitting an issue.
- We would appreciate more people joining us in maintaining this list of papers.
- Note that papers without open-source code are not recommended.
- We sincerely thank the following people for contributing to this list: [Lingfeng Yang](https://scholar.google.com/citations?user=RLhH0jwAAAAJ&hl=en), [Ge Wu](https://github.com/Martinser), [Jiazuo Yu](https://scholar.google.com/citations?user=FPoIE0UAAAAJ&hl=zh-CN), [Qiji Ma](https://github.com/StellarOdys2ey)[[List](https://github.com/StellarOdys2ey/Generalizable-Prompt-Learning-for-VLMs)]
## Keywords
 Use text-based prompts/adapters.
 Use image-based prompts/adapters.
 Use text- and image-based prompts/adapters.
## Surveys
- A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models. [[Paper](https://arxiv.org/abs/2307.12980)]
- Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey. [[Paper](https://arxiv.org/abs/2402.02242)]
- Generalizable Prompt Learning of CLIP: A Brief Overview. [[Paper](https://arxiv.org/abs/2503.01263)]
## Foundation Models
- `CLIP` **Learning Transferable Visual Models From Natural Language Supervision.** ICML 2021.
[[Paper](https://arxiv.org/abs/2103.00020)] [[Code](https://github.com/OpenAI/CLIP)]
- `ALIGN` **Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision.** ICML 2021.
[[Paper](https://arxiv.org/abs/2102.05918)]
- `LiT` **LiT: Zero-Shot Transfer with Locked-image text Tuning.** CVPR 2022.
[[Paper](https://arxiv.org/abs/2111.07991)] [[Code](https://github.com/google-research/vision_transformer#lit-models)]
- `EVA-CLIP` **EVA-CLIP: Improved Training Techniques for CLIP at Scale.** 2023.
[[Paper](https://arxiv.org/abs/2303.15389)] [[Code](https://github.com/baaivision/EVA)]
- `SigLIP` **Sigmoid Loss for Language Image Pre-Training.** ICCV 2023.
[[Paper](https://arxiv.org/abs/2303.15343)] [[Code](https://github.com/google-research/big_vision)]
- `AlphaCLIP` **Alpha-CLIP: A CLIP Model Focusing on Wherever You Want.** CVPR 2024.
[[Paper](https://arxiv.org/abs/2312.03818)] [[Code](https://github.com/SunzeY/AlphaCLIP)]
- `CLIP-KD` **CLIP-KD: An Empirical Study of CLIP Model Distillation.** CVPR 2024.
[[Paper](https://arxiv.org/abs/2307.12732)] [[Code](https://github.com/winycg/CLIP-KD)] [[论文解读](https://www.zhihu.com/question/646919153/answer/3553439426)]
- `LongCLIP` **Long-CLIP: Unlocking the Long-Text Capability of CLIP.** ECCV 2024.
[[Paper](https://arxiv.org/abs/2403.15378)] [[Code](https://github.com/beichenzbc/Long-CLIP)]
- `CLIP-Refine` **Post-pre-training for Modality Alignment in Vision-Language Foundation Models.** CVPR 2025.
[[Paper](https://arxiv.org/abs/2504.12717)] [[Code](https://github.com/yshinya6/clip-refine)]
- `KUEA` **Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models.** ICML 202Excerpt of 45,825 characters
Read on GitHub
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:56caec38debf31cc, topic:vision-language-model, desc:vision-language, readme:vision-language
matched fp:56caec38debf31cc, readme:fine-tuning, readme:fine tuning, readme:parameter-efficient