A comprehensive survey of Vision--Language Models: Pretrained models, fine-tuning, prompt engineering, adapters, and benchmark datasets

Schematic overview of the survey design highlighting optimization strategies in VLM. The figure categorizes key components covered in the paper, including fine-tuning techniques, prompt engineering, adapter and pretraining model. Each component is critically analyzed to provide comprehensive insights into current trends, challenges, and future research directions in VLM optimization.

Conceptual model of the paper.

Abstract:

Vision Language Models (VLMs) have significantly advanced multimodal tasks like image captioning, visual question answering, and multimodal retrieval. This survey presents a systematic review of 115 published papers from 2018 to 2025. It focuses key VLM components including fine-tuning strategies, prompt engineering techniques, pre-trained models, adapter modules, and benchmarking datasets. For each component, we present taxonomies and summarize comparative findings across standard VLM benchmarks. The survey emphasizes the role of lightweight, parameter-efficient adaptation methods in reducing computational overhead while maintaining strong task performance, particularly in real-world deployment contexts. It further examines the strengths and limitations of prompt-based learning, dataset-specific tuning strategies, and architectural trade-offs. Finally, the paper identifies open challenges in scalability, generalization, and bias, and explores emerging research directions including symbolic reasoning, multilingual adaptation, and energy-efficient VLM design. To our best knowledge, this is the first comprehensive survey to integrate these critical components into a single, cohesive survey paper, intended to serve as a foundational resource for researchers and practitioners striving to optimize VLMs for diverse real-world scenarios. The highlights of the review are available at https://github.com/SufyanDanish/VLM-Survey-

📌 Journal: Information Fusion

Paper Link: Click Here

Vision-Language Models Repository

This repository provides the latest collection of resources for Vision-Language Models (VLMs). It covers key components essential for research and development in the Vision language model.

📌 Components

• Fine-Tuning:
• Pre-trained Models:
• Prompt Engineering:
• Adapters:
• Benchmark Datasets:

Contributing

This repository aims to assist researchers and developers by providing well-organized resources and references to support ongoing work in Vision-Language Modeling (VLM). If you have recently published or come across a paper related to any key component of VLM — such as pretrained models, fine-tuning strategies, prompt engineering, adapters, or benchmark datasets — please feel free to open a pull request or contact us. We’ll be happy to include it in the resource list.

How to Submit a Pull Request

1. Fork the repository into your own GitHub account.

2. Add the new paper to the README.md using the following format:

   | [Title](Paper Link) | Conference | [Code/Project](Code/Project link) |
   

Summary of Prompt Engineering Techniques for Vision Language Models (2021–2025)

Year	Method	Type	Description	Publication	Code
2021	CLIP	Hard-Prompt	Introduced contrastive language-image pre-training.	ICML 2021	Link
2022	CoOp	Soft-Prompt	Context Optimization for prompt tuning using learnable embeddings.	IJCV 2022	Link
2022	CPT	Hard-Prompt	Task-specific fine-tuning in vision-language tasks.	-	Link
2022	DenseCLIP	Text Soft-Prompt	Extended CLIP to dense vision tasks with optimized textual prompts.	CVPR 2022	Link
2022	FewVLM	Hard-Prompt	Few-shot learning framework for VLMs using hard prompts.	ACL 2022	Link
2022	ProDA	Text Soft-Prompt	Prompt distribution alignment for domain adaptation.	CVPR 2022	Link
2022	ProGrad	Text Soft-Prompt	Gradient optimization to improve prompt effectiveness.	CVPR 2022	Link
2022	PEVL	Hard-Prompt	Combined prompt tuning with vision encoders for enhanced alignment.	EMNLP 2022	Link
2022	VPT	Visual Soft-Prompt	Visual embeddings as learnable prompts.	ECCV 2022	Link
2022	TPT	Text Soft-Prompt	Enhanced text-based prompt-tuning methods.	NeurIPS 2022	Link
2023	ViPT	Visual Soft-Prompt	Visual Prompt multi-modal Tracking for various downstream tasks.	CVPR 2023	Link
2023	MaPLe	Visual-text & Modal-Prompt	Multi-modal Adaptive Prompt Learning.	CVPR 2023	Link
2023	KgCoOp	Text Soft-Prompt	Knowledge-guided Context Optimization.	CVPR 2023	Link
2023	LASP	Text Soft-Prompt	Text-to-Text Optimization for Language-Aware Soft Prompting.	CVPR 2023	Link
2023	DAM-VP	Visual Soft-Prompt	Diversity-Aware Meta Visual Prompting.	CVPR 2023	Link
2023	TaskRes	Text Soft-Prompt	Task Residual for Tuning Vision-Language Models.	CVPR 2023	Link
2023	RPO	Text Hard-Prompt	Read-only Prompt Optimization for Few-shot Learning.	ICCV 2023	Link
2023	PromptSRC	Visual-Text Soft-Prompt	Semantic-Rich Contextual Prompting.	ICCV 2023	Link
2024	DePT	Visual-Text Soft-Prompt	Dense Prompt Tuning.	CVPR 2024	Link
2024	TCP	Text Soft-Prompt	Text-Conditioned Prompting.	CVPR 2024	Link
2024	MMA	Visual-Text Soft-Prompt	Multi-Modal Adaptive Prompting.	CVPR 2024	Link
2024	HPT	Visual-Text Soft-Prompt	Hierarchical Prompt Tuning.	AAAI 2024	Link
2024	CoPrompt	Soft-Prompt	Contextual Prompt Learning.	ICLR 2024	Link
2024	CasPL	Visual-Text Soft-Prompt	Cascade Prompt Learning.	ECCV 2024	Link
2024	PromptKD	Visual-Text Soft-Prompt	Knowledge Distillation-based Prompt Tuning.	CVPR 2024	Link
2025	DPC	Visual-Text Soft-Prompt	Dual-Prompt Collaboration for tuning VLMs.	CVPR 2025	Link
2025	2SFS	Visual-Text Soft-Prompt	Two-Stage Few-Shot adaptation for VLMs.	CVPR 2025	Link
2025	MMRL	Visual-Text Soft-Prompt	Multi-Modal Representation Learning	CVPR 2025	Link
2025	NLPrompt	Text Soft-Prompt	Noise-Label Prompt Learning	CVPR 2025	Link
2025	TAC	Text Soft-Prompt	Task-Aware Clustering for Prompting	CVPR 2025	Link
2025	TextRefiner	Text Soft-Prompt	Internal visual features as prompt refiners	AAAI 2025	Link
2025	ProText	Text Soft-Prompt	Prompting with text-only supervision	AAAI 2025	Link

Comparative Overview of Vision-Language Models (Pre-2023 vs Post-2023)

Model	Year	Fine-Tuning Strategy	Architecture	Pretraining Objective	Pretrained Backbone Model	Vision Encoder / Tokenizer	Parameters	Training Data	Key Innovations
ViLBERT	2019	Full fine-tuning	Two-stream Transformer	Image-text alignment with co-attentional modules	BERT	Object-based features (Faster R-CNN)	110M	COCO, Conceptual Captions	Co-attentional streams for image-text fusion
VisualBERT	2019	Full fine-tuning	Single-stream Transformer	Masked language modeling with visual embeddings	BERT	Object-based features (Faster R-CNN)	110M	COCO, VQA	Shared encoder for text and image inputs
CLIP	2021	Zero-shot inference	Encoder-decoder	Contrastive image-text learning	Pretrained from scratch	ViT/ResNet	63M–355M	400M web image-text pairs	Contrastive learning with dual encoders; broad generalization via natural language supervision
ALIGN	2021	Zero-shot inference	Dual encoder (EffNet-L2 + Transformer)	Contrastive image-text alignment	EfficientNet-L2	EfficientNet	1.8B	1B+ noisy web image-text pairs	Large-scale noisy training with CLIP-style contrastive objectives
SimVLM	2021	Full fine-tuning	Unified Transformer encoder-decoder	Prefix language modeling (unified vision-text sequence)	BERT + ResNet	Unified Transformer	1B+	Vision-language pairs	Simplified architecture with prefix modeling and no region-level supervision
Florence	2022	Full fine-tuning	Unified Transformer	Unified encoder with multi-task supervision	Swin Transformer	Swin	892M	Multilingual web-scale dataset	High-performance universal VL encoder
Flamingo	2022	Few-shot in-context learning	Perceiver Resampler + Decoder-only Transformer	Frozen vision-language backbones with trainable cross-attention	Chinchilla	ViT-L/14 + Perceiver	80B	M3W, ALIGN	In-context few-shot learning with frozen backbones and cross-modal fusion
BLIP-2	2023	Modular fine-tuning via Q-Former	Vision encoder + frozen LLM + Q-Former	Two-stage: vision-text + vision-to-language generation	ViT-G + OPT/FlanT5	ViT-G + Q-Former	223M–400M	WebLI, COCO, CC3M, CC12M	Q-Former for modular downstream tasks
IDEFICS	2023	Parameter-efficient tuning	Unified Transformer with vision encoder	Instruction-tuned vision-language	OPT + ViT	ViT	80B	COCO, VQAv2, A-OKVQA	Open-source instruction-following VLM
PaliGemma 2	2024	LoRA, fine-grained adapters	Transformer encoder-decoder	Multilingual + synthetic datasets	Gemma + ViT	ViT	-	Synthetic + real data (DOCCI, LAION, CC12M)	Multilingual generation + grounding
Gemini 2.0	2024	Modular fine-tuning	PaLM-based encoder-decoder + vision module	Multimodal pretraining with sparse transformers	PaLM 2 + ViT	ViT	-	Multilingual, synthetic corpus	Flexible and efficient multimodal reasoning
Kosmos-2.5	2024	Selective fine-tuning	Decoder-only Transformer with ViT + resampler	Document text recognition + image-to-Markdown generation	-	ViT-G/14, ViT-L/14	1.3B	Document images, OCR, structured markup data	Layout-aware multimodal literacy via visual-text fusion with Markdown generation
GPT-4V	2024	No tuning (chat interface)	Unified Transformer with vision-text fusion	Text + image pretraining	GPT-4	Custom ViT-like encoder	-	Vision-language aligned corpus	GPT-4 vision support with image-text joint encoding
Claude 3 Opus	2024	Supervised fine-tuning via API	Encoder-decoder transformer	Proprietary encoder-decoder	Proprietary	-	-	Multimodal benchmarks	Safe and high-performance multimodal chat
LongVILA	2024	Efficient parameter tuning	Video-based encoder-decoder transformer	Video-language transformer	Custom video model	Patch + frame tokenizer	-	Long video, image sequences	Long-context video QA and interleaved image-text reasoning
Molmo	2024	Instruction tuning	Encoder-decoder transformer	Transformer-based VLM	-	ViT-L/14 (CLIP)	72B	Open PixMo data	Open-source transparent training
Qwen 2.5 VL	2025	Instruction tuning	Transformer decoder with visual patch input	Vision transformer + LLM fusion	Qwen 2.5 + ViT	ViT	3B/7B/72B	Docs, images, audio	OCR + document QA specialization
DeepSeek Janus	2025	Adapter-based fine-tuning	Dual-stream Transformer with MoE	Multimodal instruction-following	DeepSeek + ViT	ViT	7B	Instruction + synthetic datasets	Efficient MoE-based dual-stream VLM
MiniCPM-o 2.6	2025	Plug-in modules + instruction tuning	Modular lightweight Transformer	Multimodal instruction-following + OCR	MiniCPM + LLaMA3	Vision adapter	8B	Instruction-tuned corpus	GPT-4V-level OCR + real-time video understanding on-device
Moondream	2025	Minimal fine-tuning	Decoder-only Transformer	Multimodal pretraining	-	Compact encoder	1.86B	Open efficient datasets	Small footprint with privacy focus
Pixtral	2025	Instruction tuning	Dual-stream compact transformer	Mistral-style ViT + LLM	Mistral + ViT	ViT	12B	Multi-domain open-source corpus	ViT fusion in compact architecture

Dataset Audit Table: Overview of Key Datasets Used in Vision-Language Research

Dataset	Size	Modalities	Language(s)	Category Diversity	Known Biases / Limitations
MS COCO	328K images	Image-Text	English	91 object categories	Western-centric content; limited cultural diversity; object-centric focus
VQAv2	204K images; 1.1M Q&A pairs	Image-QA	English	Everyday scenes with varied Q&A	Language bias; answer priors; question redundancy
RadGraph	221K reports; 10.5M annotations	Text (Radiology reports)	English	Radiology findings	Domain-specific; requires medical expertise for annotation; limited to chest X-rays
GQA	113K images; 22M questions	Image-QA	English	Compositional reasoning	Synthetic question generation; potential over-reliance on scene graphs
GeoBench-VLM	10K+ tasks	Satellite-Text	English	Natural disasters, terrain, infrastructure	Sparse labels; coverage gaps
SBU Captions	1M images	Image-Text	English	Web-sourced everyday scenes	Noisy captions; duplicate entries
MIMIC-CXR	377K images; 227K studies	Image-Text	English	Chest X-rays	Hospital-centric; privacy restrictions
EXAMS-V	20,932 questions	Mixed Multimodal	11 languages	Exam-style reasoning across disciplines	Regional bias; multilingual challenge
RS5M	5M images	Satellite-Text	English	Remote sensing imagery	Sparse labels; class imbalance; varying image quality
VLM4Bio	30K instances	Image-Text-QA	English	Biodiversity, taxonomy	Domain-specific; taxonomic bias; limited generalizability
PMC-OA	~1.65M image-text pairs	Image-Text-QA	English	High diversity within the biomedical domain; Covers a wide range of diagnostic procedures, disease types, and medical findings	Caption noise; requires medical expertise
WebLI-100B	100 Billion image-text pairs	Image-Text	100+ languages	Global content	Cultural/geographic bias; noisy data

Datasets for Vision-Language Models

Dataset Type	Dataset Name	Description	Applications	Link
Detection	COCO	330k images with annotations for detection and segmentation.	Object detection, instance segmentation, image captioning.	Link
Detection	Open Images	9M+ annotated images for detection.	Object detection, captioning, visual relationship detection.	Link
Classification	ImageNet	14M labeled images across 1K classes.	Image classification, transfer learning.	Link
Classification	Visual Genome	108k images with scene graphs and object annotations.	VQA, object detection, scene understanding.	Link
Segmentation	ADE20K	20k images labeled across 150 categories.	Semantic segmentation, scene parsing.	Link
Segmentation	Cityscapes	Urban scenes with pixel-level annotations.	Autonomous driving, semantic segmentation.	Link
Text-to-Image	Flickr30k	31k images with 5 captions each.	Image captioning, text-to-image generation.	Link
Text-to-Image	COCO Captions	Subset of COCO with image captions.	Captioning, text-image synthesis.	Link
Multimodal Alignment	VQA	200k+ questions over 100k images.	Visual QA, multimodal reasoning.	Link
Multimodal Alignment	EndoVis-18-VLQA	QA pairs for surgical/endoscopic videos.	Medical QA, surgical assistance.	Link
Multimodal Alignment	VLM4Bio	469k QA pairs, 30k images for biodiversity tasks.	Scientific QA, bio-research.	Link
Multimodal Alignment	MS-COCO Text-Only	Captions-only version of MS-COCO.	Text-based retrieval, text-image matching.	Link
Pre-training	Conceptual Captions	3.3M web-sourced image-caption pairs.	Vision-language pretraining.	Link
Pre-training	PathQA Bench Public	456k pathology QA pairs for PathChat.	Pathology education, clinical AI.	Link
Pre-training	SBU Captions	1M web-collected image-caption pairs.	Captioning, multimodal learning.	Link
Multimodal Retrieval	Flickr30k Entities	Object-level annotations on Flickr30k.	Caption alignment, image-text retrieval.	Link
Multimodal Retrieval	RS5M	5M satellite images with English descriptions.	Remote sensing, domain-specific VLM tuning.	Link
Multimodal Retrieval	v-SRL	Visual elements annotated with semantic roles.	Multimodal grounding, semantic role labeling.	Link
Multimodal Reasoning	CLEVR	Synthetic QA over generated scenes.	Visual reasoning, compositional QA.	Link
Multimodal Reasoning	GMAI-MMBench	284 datasets across 38 medical modalities.	Medical QA, clinical AI benchmarking.	Link
Multimodal Reasoning	NavGPT-Instruct-10k	10k steps for navigation-based QA.	Navigational reasoning, autonomous systems.	Link
Multimodal Reasoning	GQA	22M compositional QA pairs.	Visual reasoning, scene-based QA.	Link
Multimodal Reasoning	EXAMS-V	20,932 multilingual questions in 20 subjects.	Multilingual education QA, model benchmarking.	Link
Multimodal Reasoning	MMVP-VLM	QA pairs for evaluating pattern understanding.	Visual pattern QA, image-text alignment.	Link
Semantic Segmentation	ADE20K	20k images across diverse scenes.	Semantic segmentation, scene understanding.	Link
Semantic Segmentation	Cityscapes	Urban street views with labels.	Road scene analysis, autonomous driving.	Link
Cross-Modal Transfer	MIMIC-CXR	Chest X-rays + radiology reports.	Clinical VLMs, cross-modal training.	Link
Cross-Modal Transfer	MedNLI	Medical Natural Language Inference dataset.	Textual reasoning in medicine.	Link

Overview of Vision-Language Datasets for the Medical Domain

Dataset Name	Image-Text Pairs	QA Pairs	Description	Application	Link
VQA-Med 2020	❌	✅	Dataset for VQA and VQG in radiology; includes questions about abnormalities and image-based question generation.	Medical diagnosis, clinical decision support, multimodal QA	Link
ROCO	✅	❌	Multimodal dataset from PubMed Central with radiology/non-radiology images, captions, keywords, and UMLS concepts.	Captioning, classification, retrieval, VQA	Link
VQA-Med 2019	❌	✅	3,200 radiology images with 12,792 QA pairs across 4 categories: Modality, Plane, Organ system, Abnormality.	Medical image analysis, radiology AI, education	Link
MIMIC-NLE	✅	❌	377K chest X-rays with structured labels derived from free-text radiology reports.	Image understanding, NLP for radiology, decision support	Link
SLAKE	❌	✅	Bilingual Med-VQA dataset with semantic labels and a medical knowledge base, covering many body parts and modalities.	Annotations, diverse QA, knowledge-based AI	Link
GEMeX	✅	✅	Largest chest X-ray VQA dataset with explainability annotations to enhance visual reasoning in healthcare.	Med-VQA, visual reasoning, explainable AI	Link
MS-CXR	✅	❌	1,162 image-sentence pairs with bounding boxes and phrases for 8 findings, supporting semantic modeling.	Radiology annotation, contrastive learning, semantic modeling	Link
MedICaT	✅	❌	217K figures from 131K medical papers with captions, subfigure tags, and inline references.	Captioning, multimodal learning, retrieval	Link
3D-RAD	❌	✅	3D Med-VQA dataset using 4,000+ CT scans and 12,000+ QA pairs, including anomaly and temporal tasks.	3D VQA, multi-temporal diagnosis, 3D understanding	Link
ImageCLEFmed-MEDVQA-GI	✅	✅	10K+ endoscopy images with 30K+ (synthetic) QA pairs, focused on gastrointestinal diagnosis.	GI image analysis, synthetic data, endoscopy VQA	Link
BIOMEDICA	✅	❌	Over 24M image-text pairs from 6M biomedical articles across various disciplines, for generalist VLMs.	Biomedical VLM pretraining, retrieval, generalist AI	Link
RadGraph	❌	❌	Annotated chest X-ray reports with clinical entities and relations; structured knowledge from unstructured text.	Info extraction, knowledge graphs, NLP for radiology	Link
PMC-OA	✅	❌	1.6M image-caption pairs from PubMed Central OA articles; used in PMC-CLIP training.	Medical retrieval, classification, multimodal learning	Link
ReasonMed	❌	✅	370K VQA samples for complex reasoning, generated via multi-agent CoT for explainable answers.	Medical reasoning, clinical QA, explainable AI	Link
Lingshu	✅	✅	Aggregates 9.3M samples from 60+ datasets for generalist Med-VLMs across QA, reporting, and consultation.	Multimodal QA, report generation, medical dialogue	Link
GMAI-VL-5.5M	✅	✅	5.5M medical image-text pairs merged from multiple datasets, for general AI and clinical decision tasks.	General medical AI, QA, diagnosis, multimodal systems	Link

Table 11: Comparison of Models Across Image Captioning, VQA, and Retrieval Tasks

Model	Task	Dataset	Metric	Score
Unified VLP [287]	Image Captioning	COCO, Flickr30K	BLEU-4 / CIDEr	36.5 / 116.9 (COCO), 30.1 / 67.4 (Flickr)
VinVL [288]	Image Captioning	COCO	BLEU-4 / CIDEr	40.9 / 140.9
SimVLM [289]	Image Captioning	COCO	BLEU-4 / CIDEr	40.3 / 143.3
BLIP [35]	Image Captioning	COCO	BLEU-4 / CIDEr	41.7 / 143.5
RegionCLIP [290]	Image Captioning	COCO	BLEU-4 / CIDEr	40.5 / 139.2
BLIP-2 [236]	Image Captioning	COCO, NoCaps	BLEU-4 / CIDEr	43.7 / 123.7 (COCO), – (NoCaps)
FIBER [291]	Image Captioning	COCO	CIDEr	42.8
NLIP [292]	Image Captioning	Flickr30K	CIDEr	135.2
LCL [293]	Image Captioning	COCO	CIDEr	87.5
Unified VLP [287]	VQA	VQA 2.0	VQA Score	70.3%
VinVL [288]	VQA	VQA 2.0	VQA Score	76.6%
FewVLM [123]	VQA	VQA 2.0	VQA Score	51.1%
SimVLM [289]	VQA	VQA 2.0	VQA Score	24.1%
BLIP [35]	VQA	VQA 2.0	VQA Score	77.5%
BLIP-2 [236]	VQA	VQA 2.0	VQA Score	79.3%
VILA [164]	VQA	VQA 2.0, GQA	VQA Score	80.8% (VQA 2.0), 63.3% (GQA)
LCL [293]	VQA	VQA 2.0	VQA Score	73.4%
TCL [294]	Image Retrieval	COCO, Flickr30K	R@1	62.3% / 88.7%
CLIP [5]	Image Retrieval	COCO, Flickr30K	R@1	58.4% / 88.0%
NLIP [292]	Image Retrieval	COCO	R@1	82.6%
Cross-Attn [295]	Image Retrieval	COCO, Flickr30K	R@1	67.8% / 88.9%
DreamLIP [296]	Image Retrieval	COCO, Flickr30K	R@1	58.3% / 87.2%

🚀 Contributions and suggestions are welcome!
Contact: sufyandanish@sju.ac.kr

Please the

Danish, Sufyan, Abolghasem Sadeghi-Niaraki, Samee Ullah Khan, L. Minh Dang, Lilia Tightiz, and Hyeonjoon Moon.
A comprehensive survey of Vision-Language Models: Pretrained models, fine-tuning, prompt engineering, adapters, and benchmark datasets.
Information Fusion, Elsevier, 2025, p. 103623.
BibTeX

BibTeX

@article{danish2025comprehensive,
  title={A comprehensive survey of Vision-Language Models: Pretrained models, fine-tuning, prompt engineering, adapters, and benchmark datasets},
  author={Danish, Sufyan and Sadeghi-Niaraki, Abolghasem and Khan, Samee Ullah and Dang, L Minh and Tightiz, Lilia and Moon, Hyeonjoon},
  journal={Information Fusion},
  pages={103623},
  year={2025},
  publisher={Elsevier}
}