mlsanityData to 14 Oct 2026

Visual Tuning

arXiv 2305.06061 ↗First posted 10 May 2023cs.CV cs.AI cs.LGcited by 34

Not on the map. Why?

Abstract (arXiv)

Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the whole pre-trained model or just the fully connected layer. Instead, recent advances can achieve superior performance than full-tuning the whole pre-trained parameters by updating far fewer parameters, enabling edge devices and downstream applications to reuse the increasingly large foundation models deployed on the cloud. With the aim of helping researchers get the full picture and future directions of visual tuning, this survey characterizes a large and thoughtful selection of recent works, providing a systematic and comprehensive overview of existing work and models. Specifically, it provides a detailed background of visual tuning and categorizes recent visual tuning techniques into five groups: prompt tuning, adapter tuning, parameter tuning, and remapping tuning. Meanwhile, it offers some exciting research directions for prospective pre-training and various interactions in visual tuning.

Key facts. Cited by 34 papers in the map (data through 14 Oct 2026). Builds on 233 of its 320 references that are in the map. First posted on arXiv 10 May 2023.

Builds on

233 of 320 references.

References · 233

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  4. 2020Language Models are Few-Shot Learnerscited by 32,419
  5. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  6. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  7. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  8. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  9. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  10. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  11. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  12. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
38 more
  1. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  2. 2022Scalable Diffusion Models with Transformerscited by 6,054
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  5. 2020Training data-efficient image transformers & distillation through attentioncited by 4,586
  6. 2022Flamingo: a Visual Language Model for Few-Shot Learningcited by 4,386
  7. 2014Going Deeper with Convolutionscited by 4,241
  8. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
  9. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  10. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,864
  11. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,300
  12. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,270
  13. 2021Learning to Prompt for Vision-Language Modelscited by 2,281
  14. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  15. 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,071
  16. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,962
  17. 2017The Kinetics Human Action Video Datasetcited by 1,909
  18. 2018DARTS: Differentiable Architecture Searchcited by 1,798
  19. 2022Visual Prompt Tuningcited by 1,681
  20. 2014FitNets: Hints for Thin Deep Netscited by 1,653
  21. 2022Conditional Prompt Learning for Vision-Language Modelscited by 1,433
  22. 2022VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Trainingcited by 1,322
  23. 2022CoCa: Contrastive Captioners are Image-Text Foundation Modelscited by 1,274
  24. 2021BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-modelscited by 1,245
  25. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,209
  26. 2019A Comprehensive Survey on Transfer Learningcited by 1,160
  27. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,114
  28. 2021Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNetcited by 1,101
  29. 2021Video Swin Transformercited by 1,091
  30. 2021CLIP-Adapter: Better Vision-Language Models with Feature Adapterscited by 1,026
  31. 2021Transformers in Vision: A Surveycited by 1,013
  32. 2017Revisiting Unreasonable Effectiveness of Data in Deep Learning Eracited by 919
  33. 2021Open-vocabulary Object Detection via Vision and Language Knowledge Distillationcited by 906
  34. 2019Relational Knowledge Distillationcited by 889
  35. 2021Towards a Unified View of Parameter-Efficient Transfer Learningcited by 872
  36. 2017A Survey on Multi-Task Learningcited by 840
  37. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 837
  38. 2022A Generalist Agentcited by 808

50 most cited of 233 All 233 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 9; 2024: 12; 2025: 10; 2026: 3.

34 papers.

Sort:

By area:

  1. 2024Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directionscited by 77
  2. 2023Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurtscited by 58
  3. 2023GLA-GCN: Global-local Adaptive Graph Convolutional Network for 3D Human Pose Estimation from Monocular Videocited by 47
  4. 2023A New Learning Paradigm for Foundation Model-based Remote Sensing Change Detectioncited by 47
  5. 2024Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learningcited by 43
  6. 2024Improving Diffusion-Based Image Synthesis with Context Predictioncited by 32
  7. 2024Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptationcited by 25
  8. 2023Parameter-efficient Tuning of Large-scale Multimodal Foundation Modelcited by 23
  9. 2023DualCoOp++: Fast and Effective Adaptation to Multi-Label Recognition with Limited Annotationscited by 22
  10. 2024FMint: Bridging Human Designed and Data Pretrained Models for Differential Equation Foundation Modelcited by 19
  11. 2024Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognitioncited by 16
  12. 2024Vision-Language Models are Strong Noisy Label Detectorscited by 16
22 more
  1. 2024A Survey of Generative Techniques for Spatial-Temporal Data Miningcited by 14
  2. 2025Object Detection with Multimodal Large Vision-Language Models: An In-depth Reviewcited by 14
  3. 2023When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence Systemcited by 13
  4. 2025PRO-VPT: Distribution-Adaptive Visual Prompt Tuning via Prompt Relocationcited by 9
  5. 2023GIST: Improving Parameter Efficient Fine Tuning via Knowledge Interactioncited by 8
  6. 2023MoSA: Mixture of Sparse Adapters for Visual Efficient Tuningcited by 6
  7. 2024ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shiftcited by 5
  8. 2025LIFT+: Lightweight Fine-Tuning for Long-Tail Learningcited by 5
  9. 2024LoSA: Long-Short-range Adapter for Scaling End-to-End Temporal Action Localizationcited by 4
  10. 2024Adapt PointFormer: 3D Point Cloud Analysis via Adapting 2D Visual Transformerscited by 4
  11. 2025Omni-Mol: Multitask Molecular Model for Any-to-any Modalitiescited by 3
  12. 2025ViT-Split: Unleashing the Power of Vision Foundation Models via Efficient Splitting Headscited by 2
  13. 2025State Space Prompting via Gathering and Spreading Spatio-Temporal Information for Video Understandingcited by 2
  14. 2023When Parameter-efficient Tuning Meets General-purpose Vision-language Modelscited by 1
  15. 2024Understanding Robustness of Parameter-Efficient Tuning for Image Classificationcited by 1
  16. 2025WRT-SAM: Foundation Model-Driven Segmentation for Generalized Weld Radiographic Testingcited by 0
  17. 2025Evaluating Vision Language Model Adaptations for Radiology Report Generation in Low-Resource Languagescited by 0
  18. 2025Time-Contrastive Pretraining for In-Context Image and Video Segmentationcited by 0
  19. 2025Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysiscited by 0
  20. 20261%>100%: High-Efficiency Visual Adapter with Complex Linear Projection Optimizationcited by 0
  21. 2026Decompose, Mix, Adapt: A Unified Framework for Parameter-Efficient Neural Network Recombination and Compressioncited by 0
  22. 2026Token-Oriented Semantic Communication with Pretrained Vision Transformerscited by 0