Foundation models enable prompt-based classifiers for zero-shot and few-shot learning. Nonetheless, the conventional method of employing fixed prompts suffers from distributional shifts that negatively impact generalizability to unseen samples. This paper introduces prompt diffusion, which uses a diffusion model to gradually refine the prompts to obtain a customized prompt for each sample. Specifically, we first optimize a collection of prompts to obtain over-fitted prompts per sample. Then, we propose a prompt diffusion model within the prompt space, enabling the training of a generative transition process from a random prompt to its overfitted prompt. As we cannot access the label of a test image during inference, our model gradually generates customized prompts solely from random prompts using our trained, prompt diffusion. Our prompt diffusion is generic, flexible, and modality-agnostic, making it a simple plug-and-play module seamlessly embedded into existing prompt learning methods for textual, visual, or multi-modal prompt learning. Our diffusion model uses a fast ODE-based sampling strategy to optimize test sample prompts in just five steps, offering a good trade-off between performance improvement and computational efficiency. For all prompt learning methods tested, adding prompt diffusion yields more robust results for base-to-new generalization, cross-dataset generalization, and domain generalization in classification tasks tested over 15 diverse datasets.
MM-Prompt: Cross-Modal Prompt Tuning for Continual Visual Question Answering
Khoury College of Computer Sciences, Northeastern University（东北大学克劳尔计算机科学学院）；New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所模式识别新实验室）
Dual Modality Prompted Diffusion Priors for Zero Shot Hyperspectral Pansharpening
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Peking University（北京大学）；National Center for Applied Mathematics Shenzhen (NCAMS), Southern University of Science and Technology（南方科技大学深圳应用数学国家中心）；Department of Automation, Tsinghua University（清华大学自动化系）；National Research Council of Italy, Institute of Integrated Methodologies for Earth Observation (CNR-IMIOT)（意大利国家研究委员会综合地球观测方法研究所）
CM2-Net: Continual Cross-Modal Mapping Network for Driver Action Recognition
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Continental-NTU Corporate Lab, Nanyang Technological University（大陆集团-南洋理工大学联合企业实验室）；College of Computer Science and Technology, Harbin Engineering University（哈尔滨工程大学计算机科学与技术学院）
PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classification
Dalian University of Technology（大连理工大学）；School of Computer Science and Technology（计算机科学与技术学院）；School of Computer Science and Engineering（计算机科学与工程学院）；Dalian Minzu University（大连民族大学）；School of Information and Communication Engineering（信息与通信工程学院）
Narrating For You: Prompt-guided Audio-visual Narrating Face Generation Employing Multi-entangled Latent Space
Machine Intelligence Group, Department of CS&IS, BITS Pilani, Hyderabad Campus, India（机器智能组，计算机科学与信息学系，比斯汉学院海得拉巴校区，印度）；Georgia Institute of Technology, USA（佐治亚理工学院，美国）
Prompt as Free Lunch: Enhancing Diversity in Source-Free Cross-domain Few-shot Learning through Semantic-Guided Prompting
College of Computer and Data Science, Fuzhou University（福州大学计算机与数据科学学院）；Zhejiang University of Technology（浙江工业大学）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；INSAIT, Sofia University（保加利亚索菲亚大学INSAIT研究院）
Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（高性能计算研究所，科技研究局）；University of Minnesota（明尼苏达大学）；Microsoft Research Asia Singapore（微软亚洲研究院新加坡分部）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（Mohamed bin Zayed人工智能大学）；Australian National University（澳大利亚国立大学）；Harbin Institute of Technology（哈尔滨工业大学）
BMIP: Bi-directional Modality Interaction Prompt Learning for VLM
School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；National 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）
Token-Level Contrastive Learning with Modality-Aware Prompting for Multimodal Intent Recognition
Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Hebei University of Science and Technology（河北科技大学）
Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；School of Electronic and Information Engineering, Anhui University（安徽大学电子与信息工程学院）
