Multi-modal (vision-language) models, such as CLIP, are replacing traditional supervised pre-training models (e.g., ImageNet-based pre-training) as the new generation of visual foundation models. These models with robust and aligned semantic representations learned from billions of internet image-text pairs and can be applied to various downstream tasks in a zero-shot manner. However, in some fine-grained domains like medical imaging and remote sensing, the performance of multi-modal foundation models often leaves much to be desired. Consequently, many researchers have begun to explore few-shot adaptation methods for these models, gradually deriving three main technical approaches: 1) prompt-based methods, 2) adapter-based methods, and 3) external knowledge-based methods. Nevertheless, this rapidly developing field has produced numerous results without a comprehensive survey to systematically organize the research progress. Therefore, in this survey, we introduce and analyze the research advancements in few-shot adaptation methods for multi-modal models, summarizing commonly used datasets and experimental setups, and comparing the results of different methods. In addition, due to the lack of reliable theoretical support for existing methods, we derive the few-shot adaptation generalization error bound for multi-modal models. The theorem reveals that the generalization error of multi-modal foundation models is constrained by three factors: domain gap, model capacity, and sample size. Based on this, we propose three possible solutions from the following aspects: 1) adaptive domain generalization, 2) adaptive model selection, and 3) adaptive knowledge utilization.
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学人工智能学院）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大模型与智能治理研究重点实验室）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE（下一代智能搜索与推荐工程研究中心）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
CoLA: Cross-Modal Low-rank Adaptation for Multimodal Downstream Tasks
Centre for Vision, Speech and Signal Processing (CVSSP)（视觉、语音和信号处理中心）；University of Surrey（塞维利亚大学）；Surrey Institute for People-Centred AI（以人为本的人工智能研究所）
Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal Learning in Domain Adaptation for 3D Semantic Segmentation
Sichuan University（四川大学）；University of Electronic Science and Technology of China（电子科技大学）；Dalian University of Technology（大连理工大学）；National University of Defense Technology（国防科技大学）
Robust Multi-Modal Face Anti-Spoofing with Domain Adaptation: Tackling Missing Modalities, Noisy Pseudo-Labels, and Model Degradation
College of Computer and Cyber Security, Fujian Normal University（计算机与网络安全部，福建师范大学）；Department of Computer Science, National Tsing Hua University（计算机科学系，国立清华大学）
Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive Learning
Radboud University Medical Center（拉德堡德大学医学中心）；The Netherlands Cancer Institute（荷兰癌症研究所）；Nanjing University of Information Science and Technology（南京信息工程大学）
Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation
Northwestern University（西北大学）；Futurewei Technologies（华为未来技术研究院）；University of Electronic Science and Technology of China（电子科技大学）；University of Rochester（罗切斯特大学）
UniDiff: Parameter-Efficient Adaptation of Diffusion Models for Land Cover Classification with Multi-Modal Remotely Sensed Imagery and Sparse Annotations
Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation
The Chinese University of Hong Kong（香港中文大学）；Imsight Medical Technology Co., Ltd.（影迈医疗科技有限公司）；The Hong Kong Polytechnic University（香港理工大学）；SIAT（深圳先进技术研究院）
PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation
Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程学系）；Department of Computing, Imperial College London（帝国理工学院计算机系）；Department of Electrical Engineering and Computer Science, University of Michigan（密歇根大学电气工程与计算机科学系）；Imsight Medical Technology, Co, Ltd., China（中国医视医疗科技有限公司）；School of Data Science, Fudan University, China（复旦大学数据科学学院）
SimCMF: A Simple Cross-modal Fine-tuning Strategy from Vision Foundation Models to Any Imaging Modality
Center for Artificial Intelligence and Robotics, HKISI, CAS（中国科学院香港中文大学人工智能机器人中心）；Princeton University（普林斯顿大学）；The Hong Kong Polytechnic University（香港理工大学）；The Hong Kong University of Science and Technology（香港科技大学）；State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA（中国科学院自动化研究所多模态人工智能系统国家重点实验室）
Text-guided Foundation Model Adaptation for Long-Tailed Medical Image Classification
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；University of British Columbia（不列颠哥伦比亚大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）
Reducing Domain Gap in Frequency and Spatial domain for Cross-modality Domain Adaptation on Medical Image Segmentation
Digital Medical Research Center, School of Basic Medical Science, Fudan University（复旦大学基础医学院数字医学研究中心）；Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention（上海市医学影像计算与计算机辅助干预重点实验室）
Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation
Medical School of Nanjing University（南京大学医学院）；National Institute of Healthcare Data Science at Nanjing University（南京大学国家健康医疗大数据研究院）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Computer Science and Technology, Nanjing University of Science and Technology（南京理工大学计算机科学与技术学院）；Department of Radiology, Nanjing Drum Tower Hospital, Nanjing University Medical School（南京大学医学院附属鼓楼医院放射科）；School of Biomedical Engineering, ShanghaiTech University（上海科技大学生物医学工程学院）；Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司研发部）；Department of Artificial Intelligence, Korea University（高丽大学人工智能系）
