Awesome-Prompt-Learning-for-VLMs

A curated list of prompt learning methods for vision-language models.

Table of Contents

• Papers
  • Surveys
  • Prompt Learning
  • Test-time Prompt Tuning
  • Video Prompting

Keywords

Use text-based learnable prompts.

Use image-based learnable prompts.

Use text- and image-based learnable prompts.

Papers

Surveys

• A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models. [Paper]
• Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey. [Paper]

Prompt Learning

Experimental Comparison

Base-to-Novel Generalization. (ViT-B/16 CLIP)

Methods	Pub	Base	Novel	HM (main)	Code
CLIP	ICML 21	69.34	74.22	71.70	Link
CoOp	IJCV 22	82.69	63.22	71.66	Link
CoCoOp	CVPR 22	80.47	71.69	75.83	Link
ProDA	CVPR 22	81.56	72.30	76.65	Link
RPO	ICCV 23	81.13	75.00	77.78	Link
MaPLe	CVPR 23	82.28	75.14	78.55	Link
MetaPrompt	TIP 24	83.65	75.48	79.09	---
DePT	CVPR 24	83.62	75.04	79.10	Link
LASP	CVPR 23	83.18	76.11	79.48	---
TCP	CVPR 24	84.13	75.36	79.51	Link
PromptSRC	ICCV 23	84.26	76.10	79.97	Link
HPT	AAAI 24	84.32	76.86	80.23	Link
CoPrompt	ICLR 24	84.00	77.23	80.48	Link
PromptKD	CVPR 24	86.96	80.73	83.73	Link

Table 1. Average results on 11 datasets.

Paper List

• CoOp Learning to Prompt for Vision-Language Models. IJCV 2022.
  [Paper] [Code]
• CoCoOp Conditional Prompt Learning for Vision-Language Models. CVPR 2022.
  [Paper] [Code]
• ProDA Prompt Distribution Learning. CVPR 2022.
  [Paper] [Code]
• VPT Visual Prompt Tuning. ECCV 2022.
  [Paper] [Code]
• MaPLe MaPLe: Multi-modal Prompt Learning. CVPR 2023.
  [Paper] [Code]
• KgCoOp Visual-Language Prompt Tuningx with Knowledge-guided Context Optimization. CVPR 2023.
  [Paper] [Code]
• LASP LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision & Language Models CVPR 2023.
  [Paper]
• DAM-VP Diversity-Aware Meta Visual Prompting CVPR 2023.
  [Paper] [Code]
• TaskRes Task Residual for Tuning Vision-Language Models CVPR 2023.
  [Paper] [Code]
• RPO Read-only Prompt Optimization for Vision-Language Few-shot Learning. ICCV 2023.
  [Paper] [Code]
• KAPT Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models. ICCV 2023.
  [Paper]
• ProGrad Prompt-aligned Gradient for Prompt Tuning. ICCV 2023.
  [Paper][Code]
• PromptSRC Self-regulating Prompts: Foundational Model Adaptation without Forgetting. ICCV 2023.
  [Paper] [Code]
• DeFo Learning to Decompose Visual Features with Latent Textual Prompts. ICLR 2023.
  [Paper]
• POMP Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition NeurIPS 2023.
  [Paper] [Code]
• MetaPrompt Learning Domain Invariant Prompt for Vision-Language Models. TIP 2024.
  [Paper]
• SA2VP SA2VP: Spatially Aligned-and-Adapted Visual Prompt. AAAI 2024.
  [Paper] [Code]
• LaViP LaViP: Language-Grounded Visual Prompts. AAAI 2024.
  [Paper] [Code]
• HPT Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models. AAAI 2024.
  [Paper] [Code]
• LaViP LaViP: Language-Grounded Visual Prompts. AAAI 2024.
  [Paper]
• CoPrompt Consistency-guided Prompt Learning for Vision-Language Models. ICLR 2024.
  [Paper] [Code]
• ProText Learning to Prompt with Text Only Supervision for Vision-Language Models. arxiv 24.
  [Paper] [Code]
• PromptKD Unsupervised Prompt Distillation for Vision Language Models. CVPR 2024.
  [Paper] [Code]
• DePT DePT: Decoupled Prompt Tuning. CVPR 2024.
  [Paper] [Code]
• ArGue ArGue: Attribute-Guided Prompt Tuning for Vision-Language Models. CVPR 2024.
  [Paper]
• TCP TCP:Textual-based Class-aware Prompt tuning for Visual-Language Model. CVPR 2024.
  [Paper] [Code]

Test-time Prompt Tuning

Experimental Comparison

Methods	Pub	ImageNet	-A	-V2	-R	-S	Avg. (main)	Code
CoOp	IJCV 22	71.51	49.71	64.20	75.21	47.99	59.28	Link
CoCoOp	CVPR 22	71.02	50.63	64.07	76.18	48.75	59.91	Link
TPT	NeurIPS 22	68.98	54.77	63.45	77.06	47.94	60.81	Link
TPT+CoOp	NeurIPS 22	73.61	57.95	66.83	77.27	49.29	62.84	Link
PromptAlign	NeurIPS 23	---	59.37	65.29	79.33	59.37	63.55	Link
TPS+CoOp	Arxiv 24	73.73	60.49	66.84	77.44	49.08	65.52	Link
RLCF	ICLR 24	73.23	65.45	69.77	83.35	54.74	68.33	Link
RLCF+CoOp	ICLR 24	76.05	69.74	70.62	84.51	56.49	70.34	Link

Table 3. Test-time prompt tuning methods on OOD data.

Paper List

• TPT Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models. NeurIPS 2022.
  [Paper] [Code]
• SwapPrompt SwapPrompt: Test-Time Prompt Adaptation for Vision-Language Models. NeurIPS 2023.
  [Paper]
• PrompAlign Align Your Prompts: Test-Time Prompting with Distribution Alignment for Zero-Shot Generalization. NeurIPS 2023.
  [Paper] [Code]
• TPS Just Shift It: Test-Time Prototype Shifting for Zero-Shot Generalization with Vision-Language Models. Arxiv 2024.
  [Paper] [Code]
• RLCF Test-time Adaptation with CLIP reward for zero-shot generalization in Vision-Language Models. ICLR 2024.
  [Paper] [Code]
• InTTA Invariant Test-Time Adaptation for Vision-Language Model Generalization. Arxiv 2024.
  [Paper] [Code]

Video Prompting Learning

Experimental Comparison

Paper List

• Efficient-Prompt Prompting visual-language models for efficient video understanding. ECCV 2022.
  [Paper] [Code]
• InTTA Expanding Language-Image Pretrained Models for General Video Recognition. ECCV 2022.
  [Paper] [Code]
• RePro Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection. ICLR 2023.
  [Paper] [Code]