Conventional Unsupervised Domain Adaptation (UDA) strives to minimize distribution discrepancy between domains, which neglects to harness rich semantics from data and struggles to handle complex domain shifts. A promising technique is to leverage the knowledge of large-scale pre-trained vision-language models for more guided adaptation. Despite some endeavors, current methods often learn textual prompts to embed domain semantics for source and target domains separately and perform classification within each domain, limiting cross-domain knowledge transfer. Moreover, prompting only the language branch lacks flexibility to adapt both modalities dynamically. To bridge this gap, we propose Domain-Agnostic Mutual Prompting (DAMP) to exploit domain-invariant semantics by mutually aligning visual and textual embeddings. Specifically, the image contextual information is utilized to prompt the language branch in a domain-agnostic and instance-conditioned way. Meanwhile, visual prompts are imposed based on the domain-agnostic textual prompt to elicit domain-invariant visual embeddings. These two branches of prompts are learned mutually with a cross-attention module and regularized with a semantic-consistency loss and an instance-discrimination contrastive loss. Experiments on three UDA benchmarks demonstrate the superiority of DAMP over state-of-the-art approaches.
PromptEmbedder: Efficient and Transferable Text Embedding via Dual-LLM Soft Prompting
Department of Computer Science and Information Engineering, National Taiwan University（国立台湾大学计算机科学与资讯工程系）；National Taiwan University AI Center of Research Excellence（国立台湾大学人工智能研究中心）
DR.GAP: Mitigating Bias in Large Language Models using Gender-Aware Prompting with Decoupled Reasoning
School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；School of Computer and Cyber Sciences, Augusta University（奥古斯塔大学计算机与网络科学学院）
MOSAIC: A Multilingual, Taxonomy-Agnostic, and Computationally Efficient Approach for Radiological Report Classification
Department of Computer Science, University of Copenhagen（哥本哈根大学计算机科学系）；Neurobiology Research Unit, Copenhagen University Hospital（哥本哈根大学医院神经生物学研究单位）；Unumed Aps（Unumed公司）；Department of Diagnostic Radiology, Copenhagen University Hospital（哥本哈根大学医院诊断放射学系）；Department of Clinical Medicine, University of Copenhagen（哥本哈根大学临床医学系）；Cerebriu A/S（Cerebriu公司）；Institute for Human Genetics, Medical University of Innsbruck（因斯布鲁克医学大学人类遗传学研究所）
A Structure-Agnostic Co-Tuning Framework for LLMs and SLMs in Cloud-Edge Systems
Swinburne University of Technology（斯威本理工大学）；Tongji University（同济大学）；Wuhan University of Technology（武汉理工大学）；INFLY TECH (Shanghai) Co., Ltd.（INFLY TECH（上海）有限公司）；Wuhan University（武汉大学）；RMIT University（皇家墨尔本理工大学）
$\texttt{COSMIC}$: Mutual Information for Task-Agnostic Summarization Evaluation
International Laboratory on Learning Systems（国际学习系统实验室）；Mila - Quebec AI Institute（米拉-魁北克人工智能研究所）；McGill University（麦吉尔大学）；Université Paris-Saclay（巴黎萨克雷大学）；École de technologie supérieure (ETS)（高等技术学院（ETS））；CNRS, CentraleSupélec（法国国家科学研究中心、中央高等电力学院）
Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression
Provable Responsible AI and Data Analytics (PRADA) Lab（可证明的负责任人工智能与数据分析（PRADA）实验室）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；South China University of Technology（华南理工大学）；University of Macau（澳门大学）；Nankai University（南开大学）；University of Copenhagen（哥本哈根大学）；Stony Brook University（纽约州立大学石溪分校）；SDAIA-KAUST AI（沙特数据与人工智能总局-阿卜杜拉国王科技大学人工智能机构）；Ant Group（蚂蚁集团）
MEUV: Achieving Fine-Grained Capability Activation in Large Language Models via Mutually Exclusive Unlock Vectors
Xin Tong School of Information and Cyber Security People’s Public Security University of China（信息与网络安全学院 中国人民公安大学）；Zhi Lin School of Safety Science Tsinghua University（安全科学学院 清华大学）；Jingya Wang School of Information and Cyber Security People’s Public Security University of China（信息与网络安全学院 中国人民公安大学）；Meng Han Intelligent Fusion Research Center Zhejiang University（智能融合研究中心 浙江大学）；Bo Jin* The Third Research Institute of the Ministry of Public Security of China（中华人民共和国公安部第三研究所）
Medical Visual Prompting (MVP): A Unified Framework for Versatile and High-Quality Medical Image Segmentation
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）；School of Information Science and Technology, Guangdong University of Foreign Studies（广东外语外贸大学信息科学与技术学院）；Faculty of Science and Technology, University of Macau（澳门大学科技学院）；The First Affiliated Hospital of Sun Yat-Sen University（中山大学附属第一医院）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）；South China Hospital, Shenzhen University（深圳大学华南医院）
