Universal Multi-source Domain Adaptation (UniMDA) transfers knowledge from multiple labeled source domains to an unlabeled target domain under domain shifts (different data distribution) and class shifts (unknown target classes). Existing solutions focus on excavating image features to detect unknown samples, ignoring abundant information contained in textual semantics. In this paper, we propose an Adaptive Prompt learning with Negative textual semantics and uncErtainty modeling method based on Contrastive Language-Image Pre-training (APNE-CLIP) for UniMDA classification tasks. Concretely, we utilize the CLIP with adaptive prompts to leverage textual information of class semantics and domain representations, helping the model identify unknown samples and address domain shifts. Additionally, we design a novel global instance-level alignment objective by utilizing negative textual semantics to achieve more precise image-text pair alignment. Furthermore, we propose an energy-based uncertainty modeling strategy to enlarge the margin distance between known and unknown samples. Extensive experiments demonstrate the superiority of our proposed method.
Continual Learning on CLIP via Incremental Prompt Tuning with Intrinsic Textual Anchors
School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；Data61, CSIRO（CSIRO数据61研究中心）；School of Computer Science, University of Auckland（奥克兰大学计算机科学学院）；Australian Institute for Machine Learning (AIML), The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）
COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain Adaptation
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Rutgers University（罗格斯大学）；Amazon Web Services（亚马逊云科技）
Models should be able to adapt to unseen data during test-time to avoid performance drops caused by inevitable distribution shifts in real-world deployment scenarios. In this work, we tackle the practical yet challenging test-time adaptation (TTA) problem, where a model adapts to the target domain without accessing the source data. We propose a simple recipe called \textit{Data-efficient Prompt Tuning} (DePT) with two key ingredients. First, DePT plugs visual prompts into the vision Transformer and only tunes these source-initialized prompts during adaptation. We find such parameter-efficient …
Department of Automation, BNRist, Tsinghua University（清华大学自动化系、智能产业研究院（BNRist））；Beijing Institute of Technology（北京理工大学）；Carnegie Mellon University（卡内基梅隆大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
Unsupervised domain adaption (UDA) aims to adapt models learned from a well-annotated source domain to a target domain, where only unlabeled samples are given. Current UDA approaches learn domain-invariant features by aligning source and target feature spaces. Such alignments are imposed by constraints such as statistical discrepancy minimization or adversarial training. However, these constraints could lead to the distortion of semantic feature structures and loss of class discriminability. In this paper, we introduce a novel prompt learning paradigm for UDA, named Domain Adaptation via Promp…
FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation
Beihang University（北京航空航天大学）；Zhejiang Lab（之江实验室）；Singapore University of Technology and Design（新加坡科技设计大学）；BioMind Technology Center（BioMind技术中心）；Zhongguancun Laboratory（中关村实验室）
Prmpt2Adpt: Prompt-Based Zero-Shot Domain Adaptation for Resource-Constrained Environments
Clean and Resilient Energy System Lab (CARES), Texas A&M University（清洁而坚韧能源系统实验室（CARES），德克萨斯A&M大学）；Robotics Research Center, United States Military Academy（机器人研究中心，美国军事学院）
TCP:Textual-based Class-aware Prompt tuning for Visual-Language Model
Institute of Automation, CAS（中国科学院自动化研究所）；State Key Laboratory of Multimodal Artificial Intelligence Systems（多模态人工智能系统国家重点实验室）；State Key Lab of Processors（处理器国家重点实验室）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Tsinghua University（清华大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Columbia University（哥伦比亚大学）
Recent advances in pre-training vision-language models like CLIP have shown great potential in learning transferable visual representations. Nonetheless, for downstream inference, CLIP-like models suffer from either 1) degraded accuracy and robustness in the case of inaccurate text descriptions during retrieval-based inference (the challenge for zero-shot protocol); or 2) breaking the well-established vision-language alignment (the challenge for linear probing). To address them, we propose Decomposed Feature Prompting (DeFo). DeFo leverages a flexible number of learnable embeddings as textual …
Dynamic Textual Prompt For Rehearsal-free Lifelong Person Re-identification
National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology（空天地海一体化大数据应用技术国家工程实验室）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）
Prompt-driven Transferable Adversarial Attack on Person Re-Identification with Attribute-aware Textual Inversion
School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）；National Engineering Research Center of Robot Visual Perception and Control Technology（机器人视觉感知与控制技术国家工程研究中心）；College of Information Science and Engineering, Hunan Normal University（湖南师范大学信息科学与工程学院）
Jiangxi Normal University（江西师范大学）；University of Science and Technology of China（中国科学技术大学）；East China Normal University（华东师范大学）；Beijing Jiaotong University（北京交通大学）
Contrastive Language-Image Learning with Augmented Textual Prompts for 3D/4D FER Using Vision-Language Model
Information & Computer Science Department, King Fahd University of Petroleum & Minerals（国王法赫德石油与矿物大学信息与计算机科学系）；Center for Machine Vision and Signal Analysis, University of Oulu（奥卢大学机器视觉与信号分析中心）
Peking University（北京大学）；Huawei Technologies（华为技术有限公司）；Wuhan University（武汉大学）；The Chinese University of Hong Kong（香港中文大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
Continual Test-Time Adaptation (CTTA) aims to adapt the source model to continually changing unlabeled target domains without access to the source data. Existing methods mainly focus on model-based adaptation in a self-training manner, such as predicting pseudo labels for new domain datasets. Since pseudo labels are noisy and unreliable, these methods suffer from catastrophic forgetting and error accumulation when dealing with dynamic data distributions. Motivated by the prompt learning in NLP, in this paper, we propose to learn an image-level visual domain prompt for target domains while havi…
Institute of Automation, CAS（中国科学院自动化研究所）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）；University of Chinese Academy of Sciences(CAS)（中国科学院大学）
Prompt tuning is an effective way to adapt the pre-trained visual-language model (VLM) to the downstream task using task-related textual tokens. Representative CoOp-based work combines the learnable textual tokens with the class tokens to obtain specific textual knowledge. However, the specific textual knowledge is the worse generalization to the unseen classes because it forgets the essential general textual knowledge having a strong generalization ability. To tackle this issue, we introduce a novel Knowledge-guided Context Optimization (KgCoOp) to enhance the generalization ability of the le…
FedMVP: Federated Multimodal Visual Prompt Tuning for Vision-Language Models
University of Trento（特伦托大学）；University of Bergamo（贝拉姆奥大学）；Indian Institute of Technology Bombay（印度班加罗尔理工学院）；LNMIIT Jaipur（斋普尔LNMIIT）；The University of Queensland（昆士兰大学）；Fondazione Bruno Kessler（布鲁诺·凯塞勒基金会）
Medal S: Spatio-Textual Prompt Model for Medical Segmentation
Medical Image Insights, Shanghai, China（医学影像洞察，上海，中国）；University of Washington, Seattle, WA, USA（华盛顿大学，西雅图，华盛顿州，美国）；University of Waterloo, Waterloo, ON, Canada（滑铁卢大学，滑铁卢，安大略省，加拿大）；Xi'an Jiaotong University, Xi'an, China（西安交通大学，西安，中国）
Multi-Scale Global-Instance Prompt Tuning for Continual Test-time Adaptation in Medical Image Segmentation
School of Computer Science, University of Nottingham, UK（诺丁汉大学计算机科学学院）；School of Artificial Intelligence, Shenzhen University, China（深圳大学人工智能学院）；Department of Information Engineering and Computer Science, University of Trento, Italy（特伦托大学信息工程与计算机科学系）；School of Computer Science and Information Engineering, Hefei University of Technology, China（合肥工业大学计算机科学与信息工程学院）
Visual and Textual Prompts in VLLMs for Enhancing Emotion Recognition
School of Computing, Australian National University (ANU)（澳大利亚国立大学计算机学院）；Quriosity Pty Ltd；Human-Centric Advancements Chair in AI, Curtin University and Australian National University（Curtin大学人工智能人本发展主席职位，澳大利亚国立大学）
A Similarity Paradigm Through Textual Regularization Without Forgetting
Shanghai Jiao Tong University（上海交通大学）；Hong Kong Baptist University（香港浸会大学）；Northeastern University（东北大学）；University of Science and Technology of China（中国科学技术大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
Concept Alignment Contrast and Long-Short Prompt Memory for Test-Time Adaptation of SAM3 in Medical Image Segmentation
University of Electronic Science and Technology of China（电子科技大学）；Faculty of Information Technology, Monash University（莫纳什大学信息技术学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation
School of Computer Science and Engineering, Northwestern Polytechnical University（西北工业大学计算机科学与工程学院）；Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；Ningbo Institute of Northwestern Polytechnical University（西北工业大学宁波研究院）
A-TPT: Angular Diversity Calibration Properties for Test-Time Prompt Tuning of Vision-Language Models
Dept. of Electronic and Telecommunication Engineering, University of Moratuwa（摩图瓦大学电子与电信工程系）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
