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https://github.com/ttengwang/Awesome_Prompting_Papers_in_Computer_Vision
A curated list of prompt-based paper in computer vision and vision-language learning.
https://github.com/ttengwang/Awesome_Prompting_Papers_in_Computer_Vision
List: Awesome_Prompting_Papers_in_Computer_Vision
adapter few-shot-learning parameter-efficient-tuning prompt-learning prompt-tuning visual-prompt zero-shot-learning
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A curated list of prompt-based paper in computer vision and vision-language learning.
- Host: GitHub
- URL: https://github.com/ttengwang/Awesome_Prompting_Papers_in_Computer_Vision
- Owner: ttengwang
- Created: 2022-01-16T08:22:26.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-12-18T05:42:47.000Z (11 months ago)
- Last Synced: 2024-05-19T21:38:13.289Z (6 months ago)
- Topics: adapter, few-shot-learning, parameter-efficient-tuning, prompt-learning, prompt-tuning, visual-prompt, zero-shot-learning
- Homepage: https://visualprompting.github.io/
- Size: 72.3 KB
- Stars: 862
- Watchers: 37
- Forks: 68
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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README
# Awesome Prompting Papers in Computer Vision
A curated list of prompt-based papers in computer vision and vision-language learning.
- [Awesome Prompting Papers in Computer Vision](#awesome-prompting-papers-in-computer-vision)
- [Keywords](#keywords)
- [Vision Prompt](#vision-prompt)
- [Vision-Language Prompt](#vision-language-prompt)
- [Language-Interactable Prompt](#language-interactable-prompt)
- [Vision-Language Instruction Tuning](#vision-language-instruction-tuning)
- [More Resources](#more-resources)
### Keywords
* Task tag, e.g.,  
* Abbreviation tag, e.g., 
* Characteristic tag: Some characteristic makes this paper unique, e.g.,  
* **Bold font**: We highlight some pilot work that may contribute to the prevalence of visual prompting.
## Vision Prompt
This section collects papers prompting pretrained vision foundation models (e.g., ViT) for parameter-efficient adaptation.
- **Learning to Prompt for Continual Learning** [[paper]](https://arxiv.org/abs/2112.08654) [[code]](https://github.com/google-research/l2p)
`CVPR 2022` 
- **Visual Prompt Tuning** [[paper]](https://arxiv.org/pdf/2203.12119.pdf) [[code]](https://github.com/KMnP/vpt)
`ECCV 2022` 
- DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning [[paper]](https://arxiv.org/pdf/2204.04799.pdf) [[code]](https://github.com/google-research/l2p)
`ECCV 2022` 
- AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition [[paper]](https://arxiv.org/abs/2205.13535) [[code]](https://github.com/ShoufaChen/AdaptFormer)
`NeurIPS 2022` 
- Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning [[paper]](https://arxiv.org/abs/2210.08823) [[code]](https://github.com/dongzelian/SSF)
`NeurIPS 2022`
- P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting [[paper]](https://arxiv.org/abs/2208.02812) [[code]](https://github.com/wangzy22/P2P)
`NeurIPS 2022` 
- Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models [[paper]](https://arxiv.org/abs/2209.06970) [[code]](https://github.com/ChenWu98/Generative-Visual-Prompt)
`NeurIPS 2022` 
- Visual Prompting via Image Inpainting [[paper]]() [[code]](https://github.com/amirbar/visual_prompting)
`NeurIPS 2022`  
- Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation [[paper]](https://arxiv.org/abs/2212.04145)
`AAAI 2023`
- LPT: Long-tailed Prompt Tuning for Image Classification [[paper]](https://openreview.net/forum?id=8pOVAeo8ie)
`ICLR 2023`
- Diversity-Aware Meta Visual Prompting [[paper]](https://arxiv.org/abs/2303.08138) [[code]](https://github.com/shikiw/DAM-VP)
`CVPR 2023`
- Semantic Prompt for Few-Shot Image Recognition [[paper]](https://arxiv.org/abs/2303.14123)
`CVPR 2023` 
- Visual Prompt Tuning for Generative Transfer Learning [[paper]](https://arxiv.org/abs/2210.00990) [[code]](https://github.com/google-research/generative_transfer)
`CVPR 2023` 
- CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching [[paper]](https://arxiv.org/abs/2303.13076) [[code]](https://github.com/tgxs002/CORA)
`CVPR 2023` 
- Images Speak in Images: A Generalist Painter for In-Context Visual Learning [[paper]](https://arxiv.org/abs/2212.02499) [[code]](https://arxiv.org/abs/2212.02499)
`CVPR 2023`  
- PIVOT: Prompting for Video Continual Learning [[paper]](https://arxiv.org/abs/2212.04842)
`CVPR 2023` 
- Learning Expressive Prompting With Residuals for Vision Transformers [[paper]](https://arxiv.org/abs/2303.15591)
`CVPR 2023` 
- BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning [[paper]](https://arxiv.org/abs/2303.14773) [[code]](https://github.com/changdaeoh/BlackVIP)
`CVPR 2023` 
- Visual Prompt Multi-Modal Tracking [[paper]](https://arxiv.org/abs/2303.10826) [[code]](https://github.com/jiawen-zhu/ViPT)
`CVPR 2023` 
- A-La-Carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting [[paper]](https://arxiv.org/abs/2302.07994)
`CVPR 2023` 
- Understanding and Improving Visual Prompting: A Label-Mapping Perspective [[paper]](https://arxiv.org/abs/2211.11635) [[code]](https://github.com/OPTML-Group/ILM-VP)
`CVPR 2023`
- Visual Query Tuning: Towards Effective Usage of Intermediate Representations for Parameter and Memory Efficient Transfer Learning [[paper]](https://arxiv.org/abs/2212.03220) [[code]](https://github.com/andytu28/VQT)
`CVPR 2023`
- Explicit Visual Prompting for Low-Level Structure Segmentations low-level segmentation [[paper]](https://arxiv.org/abs/2303.10883) [[code]](https://github.com/NiFangBaAGe/Explicit-Visual-Prompt)
`CVPR 2023` 
- Understanding and Improving Visual Prompting: A Label-Mapping Perspective [[paper]](https://arxiv.org/abs/2211.11635) [[code]](https://github.com/OPTML-Group/ILM-VP)
`CVPR 2023`
**ArXiv Papers**
- **Exploring Visual Prompts for Adapting Large-Scale Models** [[paper]](https://arxiv.org/pdf/2203.17274.pdf) [[code]](https://github.com/hjbahng/visual_prompting)
`arXiv 2022/03`
- Vision Transformer Adapter for Dense Predictions [[paper]](https://arxiv.org/pdf/2205.08534.pdf) [[code]](https://github.com/czczup/ViT-Adapter)
`arXiv 2022/05`  
- Neural Prompt Search [[paper]](https://arxiv.org/abs/2206.04673) [[code]](https://github.com/Davidzhangyuanhan/NOAH)
`arXiv 2022/06`  
- Convolutional Bypasses Are Better Vision Transformer Adapters [[paper]](https://arxiv.org/abs/2207.07039) [[code]](https://github.com/JieShibo/PETL-ViT)
`arXiv 2022/07`
- Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets [[paper]](https://arxiv.org/pdf/2208.07463.pdf)
`arXiv 2022/08`
- Prompt Vision Transformer for Domain Generalization [[paper]](https://arxiv.org/pdf/2208.08914.pdf)
`arXiv 2022/08` 
- Prompt-Matched Semantic Segmentation [[paper]](https://arxiv.org/abs/2208.10159)
`arXiv 2022/08` 
- Visual Prompt Tuning for Test-time Domain Adaptation [[paper]](https://arxiv.org/abs/2210.04831)
`arXiv 2022/10`
- Visual Prompting for Adversarial Robustness [[paper]](https://arxiv.org/abs/2210.06284)
`arXiv 2022/10` 
- Prompt Generation Networks for Efficient Adaptation of Frozen Vision Transformers [[paper]](https://arxiv.org/abs/2210.06466) [[code]](https://github.com/jochemloedeman/PGN)
`arXiv 2022/10`
- Towards a Unified View on Visual Parameter-Efficient Transfer Learning [[paper]](https://arxiv.org/abs/2210.00788) [[code]](https://github.com/bruceyo/V-PETL)
`arXiv 2022/10` 
- Multitask Vision-Language Prompt Tuning [[paper]](https://arxiv.org/abs/2211.11720) [[code]](https://github.com/sIncerass/MVLPT)
`arXiv 2022/11`  
## Vision-Language Prompt
This section collects papers prompting pretrained vision-language foundation models (e.g., CLIP) for parameter-efficient adaptation.
- **Learning Transferable Visual Models From Natural Language Supervision** [[paper]](https://arxiv.org/abs/2103.00020) [[code]](https://github.com/OpenAI/CLIP)
`ICML 2021` 
- **Learning to Prompt for Vision-Language Models** [[paper]](https://arxiv.org/abs/2109.01134) [[code]](https://github.com/KaiyangZhou/CoOp)
`IJCV 2022` 
- Prompt Distribution Learning [[paper]](https://arxiv.org/pdf/2205.03340.pdf)
`CVPR 2022`
- Conditional Prompt Learning for Vision-Language Models [[paper]](https://arxiv.org/pdf/2203.05557.pdf) [[code]](https://github.com/KaiyangZhou/CoOp)
`CVPR 2022` 
- DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting [[paper]](https://arxiv.org/pdf/2112.01518.pdf) [[code]](https://github.com/raoyongming/denseclip)
`CVPR 2022`  
- Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos [[paper]](https://arxiv.org/pdf/2203.14104.pdf) [[code]](https://github.com/ttlmh/Bridge-Prompt)
`CVPR 2022`  
- PointCLIP: Point Cloud Understanding by CLIP [[paper]](https://arxiv.org/pdf/2112.02413.pdf) [[code]](https://github.com/ZrrSkywalker/PointCLIP)
`CVPR 2022` 
- VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks [[paper]](https://arxiv.org/pdf/2112.06825.pdf) [[code]](https://github.com/ylsung/VL_adapter)
`CVPR 2022`   
- A Good Prompt Is Worth Millions of Parameters? Low-resource Prompt-based Learning for Vision-Language Models [[paper]](https://arxiv.org/abs/2110.08484)
`ACL 2022`  
- Can Language Understand Depth? [[paper]](https://arxiv.org/pdf/2207.01077.pdf) [[code]](https://github.com/Adonis-galaxy/DepthCLIP)
`ACM MM 2022`  
- Expanding Language-Image Pretrained Models for General Video Recognition [[paper]](https://arxiv.org/abs/2208.02816) [[code]](https://aka.ms/X-CLIP)
`ECCV 2022` 
- Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classification [[paper]](https://arxiv.org/pdf/2207.09519.pdf) [[code]](https://github.com/gaopengcuhk/tip-adapter)
`ECCV 2022`
- OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression [[paper]](https://arxiv.org/abs/2206.02338)
`NeurIPS 2022` 
- Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models [[paper]](https://arxiv.org/pdf/2209.07511.pdf) [[code]](https://azshue.github.io/TPT/)
`NeurIPS 2022`
- Learning to Decompose Visual Features with Latent Textual Prompts [[paper]](https://openreview.net/forum?id=wtcud6HroZr)
`ICLR 2023`
- PLOT: Prompt Learning with Optimal Transport for Vision-Language Models [[paper]](https://openreview.net/forum?id=zqwryBoXYnh) [[code]](https://github.com/CHENGY12/PLOT)
`ICLR 2023`
- Visual-Language Prompt Tuning with Knowledge-guided Context Optimization [[paper]](https://arxiv.org/abs/2303.13283) [[code]](https://github.com/htyao89/KgCoOp)
`CVPR 2023` 
- Open-Set Fine-Grained Retrieval Via Prompting Vision-Language Evaluator [[paper]](https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Open-Set_Fine-Grained_Retrieval_via_Prompting_Vision-Language_Evaluator_CVPR_2023_paper.pdf)
`CVPR 2023` 
- Multimodal Prompting With Missing Modalities for Visual Recognition [[paper]](https://arxiv.org/abs/2303.03369) [[code]](https://github.com/YiLunLee/Missing_aware_prompts)
`CVPR 2023`
- Efficient Multimodal Fusion Via Interactive Prompting [[paper]](https://arxiv.org/abs/2304.06306)
`CVPR 2023` 
- Hierarchical Prompt Learning for Multi-Task Learning [[paper]](https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Hierarchical_Prompt_Learning_for_Multi-Task_Learning_CVPR_2023_paper.pdf) [[code]](https://github.com/lynlynlyn/hipro)
`CVPR 2023` 
- Text-Visual Prompting for Efficient 2D Temporal Video Grounding [[paper]](https://arxiv.org/abs/2303.04995)
`CVPR 2023` 
- VoP: Text-Video Co-operative Prompt Tuning for Cross-Modal Retrieval [[paper]](https://arxiv.org/abs/2211.12764) [[code]](https://github.com/bighuang624/VoP)
`CVPR 2023` 
- MaPLe: Multi-modal Prompt Learning [[paper]](https://arxiv.org/abs/2210.03117) [[code]](https://github.com/muzairkhattak/multimodal-prompt-learning)
`CVPR 2023`
- Texts as Images in Prompt Tuning for Multi-Label Image Recognition [[paper]](https://arxiv.org/abs/2211.12739) [[code]](https://github.com/guozix/TaI-DPT)
`CVPR 2023` 
- Vita-CLIP: Video and Text Adaptive CLIP Via Multimodal Prompting [[paper]](https://arxiv.org/abs/2304.03307) [[code]](https://github.com/TalalWasim/Vita-CLIP)
`CVPR 2023` 
- LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision & Language Models [[paper]](https://arxiv.org/abs/2210.01115) [[code]](https://www.adrianbulat.com/lasp)
`CVPR 2023`
- $\pi$-Tuning: Transferring Multimodal Foundation Models with Optimal Multi-task Interpolation [[paper]](https://arxiv.org/abs/2304.14381) [[code]](https://github.com/TencentARC/pi-Tuning)
`ICML 2023`
- POUF: Prompt-oriented unsupervised fine-tuning for large pre-trained models [[paper]](https://arxiv.org/abs/2305.00350) [[code]](https://github.com/korawat-tanwisuth/POUF)
`ICML 2023`
- Rethinking the Openness of CLIP [[paper]](https://arxiv.org/abs/2206.01986) [[code]](https://github.com/lancopku/clip-openness)
`ACL 2023` 
- PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization [[paper]](https://arxiv.org/abs/2307.15199) [[code]](https://promptstyler.github.io/)
`ICCV 2023` 
**ArXiv Papers**
- **Colorful Prompt Tuning for Pre-trained Vision-Language Models** [[paper]](https://arxiv.org/abs/2109.11797)
`arXiv 2021/08`  
- ActionCLIP: A New Paradigm for Video Action Recognition [[paper]](https://arxiv.org/abs/2109.08472) [[code]](https://github.com/sallymmx/ActionCLIP)
`arXiv 2021/09` 
- CLIP-Adapter: Better Vision-Language Models with Feature Adapters [[paper]](https://arxiv.org/abs/2110.04544) [[code]](https://github.com/gaopengcuhk/clip-adapter)
`arXiv 2021/10`
- Amortized Prompt: Lightweight Fine-Tuning for CLIP in Domain Generalization [[paper]](https://arxiv.org/abs/2111.12853)
`arXiv 2021/11` 
- Prompting Visual-Language Models for Efficient Video Understanding [[paper]](https://arxiv.org/abs/2112.04478) [[code]](https://github.com/ju-chen/Efficient-Prompt)
`arXiv 2021/12`   
- Unsupervised Prompt Learning for Vision-Language Models [[paper]](https://arxiv.org/pdf/2204.03649.pdf) [[code]](https://github.com/tonyhuang2022/UPL)
`arXiv 2022/04`  
- Prompt-aligned Gradient for Prompt Tuning [[paper]](https://arxiv.org/abs/2205.14865) [[code]](https://github.com/BeierZhu/Prompt-align)
`arXiv 2022/05`
- Parameter-Efficient Image-to-Video Transfer Learning [[paper]](https://arxiv.org/pdf/2206.13559.pdf)
`arXiv 2022/06`  
- DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations [[paper]](https://arxiv.org/abs/2206.09541)
`arXiv 2022/06` 
- Prompt Tuning for Generative Multimodal Pretrained Models [[paper]](https://arxiv.org/abs/2208.02532) [[code]](https://github.com/OFA-Sys/OFA)
`arXiv 2022/06`    
- Prompt Tuning with Soft Context Sharing for Vision-Language Models [[paper]](https://arxiv.org/pdf/2208.13474.pdf)
`arXiv 2022/08` 
- CPL: Counterfactual Prompt Learning for Vision and Language Models [[paper]](https://arxiv.org/abs/2210.10362) [[code]](https://github.com/eric-ai-lab/CPL)
`arXiv 2022/10` 
- Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models [[paper]](https://arxiv.org/abs/2211.02219) [[code]](https://github.com/machengcheng2016/Subspace-Prompt-Learning)
`arXiv 2022/10` 
- Unified Vision and Language Prompt Learning [[paper]](https://arxiv.org/abs/2210.07225)
`arXiv 2022/10`
- Multi-Prompt Alignment for Multi-source Unsupervised Domain Adaptation [[paper]](https://arxiv.org/abs/2209.15210)
`arXiv 2022/10` 
- Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition [[paper]](https://arxiv.org/abs/2304.04704) [[code]](https://github.com/amazon-science/prompt-pretraining)
`arXiv 2023/04`
### Language-Interactable Prompt
Language-interactable prompter develops zero/few-shot capabilities by prompting **several independent foundational models** (VLMs, LLMs, VMs, etc.) with the language interface. One of the most attractive applications is [multimodal chatbot](https://github.com/zjr2000/Awesome-Multimodal-Assistant).
- **Multimodal Few-Shot Learning with Frozen Language Models** [[paper]](https://arxiv.org/abs/2106.13884)
`NeurIPS 2021` 
- An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA [[paper]](https://arxiv.org/pdf/2109.05014.pdf) [[code]](https://github.com/microsoft/PICa)
`AAAI 2022` 
- VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning [[paper]](https://arxiv.org/pdf/2102.10407.pdf) [[code]](https://github.com/Vision-CAIR/VisualGPT)
`CVPR 2022` 
- **Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language** [[paper]](https://arxiv.org/pdf/2204.00598.pdf) [[code]](https://socraticmodels.github.io/#code)
`ICLR 2023`   
**Arxiv Papers**
- **Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models** [[paper]](https://arxiv.org/abs/2303.04671) [[code]](https://github.com/microsoft/TaskMatrix) [[demo]](https://huggingface.co/spaces/microsoft/visual_chatgpt)
`arXiv 2023/03`   -759CBC?)
- **Chameleon: Plug-and-play compositional reasoning with large language models** [[paper]](https://arxiv.org/abs/2304.09842) [[code]](https://github.com/lupantech/chameleon-llm)
`arXiv 2023/04`   -759CBC?)
- ClipCap: CLIP Prefix for Image Captioning [[paper]](https://arxiv.org/abs/2111.09734) [[code]](https://github.com/rmokady/CLIP_prefix_caption)
`arXiv 2021/11` 
- Flamingo: a Visual Language Model for Few-Shot Learning [[paper]](https://arxiv.org/abs/2204.14198)
`arXiv 2022/04`  
- Language Models Can See: Plugging Visual Controls in Text Generation [[paper]](https://arxiv.org/pdf/2205.02655.pdf) [[code]](https://github.com/yxuansu/MAGIC)
`arXiv 2022/05`  
- Zero-Shot Video Question Answering via Frozen Bidirectional Language Models [[paper]](https://arxiv.org/pdf/2206.08155.pdf)
`arXiv 2022/06` 
- Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning [[paper]](https://arxiv.org/pdf/2206.01843.pdf)
`arXiv 2022/06` 
### Vision-Language Instruction Tuning
The goal of vision-language instruction tuning is to train a model that can effectively understand instructions for general-purpose multimodal tasks.
- Visual Instruction Tuning [[paper]](https://arxiv.org/abs/2304.08485) [[code]](https://github.com/haotian-liu/LLaVA) [[demo]](https://llava.hliu.cc/)
`arXiv 2023/04`
- MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models [[paper]](https://arxiv.org/abs/2304.10592) [[code]](https://github.com/Vision-CAIR/MiniGPT-4) [[demo]](minigpt-4.github.io)
`arXiv 2023/04`
- Otter: A Multi-Modal Model with In-Context Instruction Tuning [[paper]](https://arxiv.org/abs/2305.03726) [[code]](https://github.com/Luodian/Otter) [[demo]](otter.cliangyu.com/)
`arXiv 2023/05`
- MultiModal-GPT: A Vision and Language Model for Dialogue with Humans [[paper]](https://arxiv.org/abs/2305.04790) [[code]](https://github.com/open-mmlab/Multimodal-GPT)
`arXiv 2023/05`
- InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning [[paper]](https://arxiv.org/abs/2305.06500) [[code]](https://github.com/salesforce/LAVIS/tree/main/projects/instructblip)
`arXiv 2023/05`
- InstructCV: Instruction-Tuned Text-to-Image Diffusion Models as Vision Generalists [[paper]](https://arxiv.org/abs/2310.00390) [[code]](https://github.com/AlaaLab/InstructCV) [[demo]](https://huggingface.co/spaces/alaa-lab/InstructCV)
`arXiv 2023/09`
## More Resources
* [PromptPapers](https://github.com/thunlp/PromptPapers): A comprehensive curated list for prompting papers (mainly in natural language processing)
* [Awesome Multimodal Assistant](https://github.com/zjr2000/Awesome-Multimodal-Assistant): a curated list for vision-language instruction tuning and LLM-based chatbot.