Recent inverse problem solvers that leverage generative diffusion priors have garnered significant attention due to their exceptional quality. However, adaptation of the prior is necessary when there exists a discrepancy between the training and testing distributions. In this work, we propose deep diffusion image prior (DDIP), which generalizes the recent adaptation method of SCD by introducing a formal connection to the deep image prior. Under this framework, we propose an efficient adaptation method dubbed D3IP, specified for 3D measurements, which accelerates DDIP by orders of magnitude while achieving superior performance. D3IP enables seamless integration of 3D inverse solvers and thus leads to coherent 3D reconstruction. Moreover, we show that meta-learning techniques can also be applied to yield even better performance. We show that our method is capable of solving diverse 3D reconstructive tasks from the generative prior trained only with phantom images that are vastly different from the training set, opening up new opportunities of applying diffusion inverse solvers even when training with gold standard data is impossible. Code: https://github.com/HJ-harry/DDIP3D
Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection
State Key Laboratory of Blockchain and Data Security（区块链与数据安全国家重点实验室）；Zhejiang Provincial Engineering Research Center for Real-Time SmartTech in Urban Security Governance（浙江省实时智能科技在城市安全治理中的工程研究中心）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江）区块链与数据安全研究院）
Noisy Test-Time Adaptation in Vision-Language Models
Hong Kong Baptist University（香港浸会大学）；Hefei University of Technology（合肥工业大学）；The University of Sydney（悉尼大学）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Carnegie Mellon University（卡内基梅隆大学）
Model-free Test Time Adaptation for Out-Of-Distribution Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Alibaba Group（阿里巴巴集团）；Peking University（北京大学）；Meta
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
City University of Hong Kong（香港城市大学）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；University of Oxford（牛津大学）；Harvard University（哈佛大学）；Nanyang Technological University（南洋理工大学）
TTL: Test-time Textual Learning for OOD Detection with Pretrained Vision-Language Models
Sun Yat-sen University（中山大学）；China United Network Communications Corporation Limited Guangdong Branch（中国联合网络通信集团有限公司广东分公司）；Peng Cheng Laboratory（鹏城实验室）；Hong Kong University of Science and Technology（香港科技大学）；Key Laboratory of Machine Intelligence and Advanced Computing, MOE（教育部机器智能与高级计算重点实验室）
Reliable Neural Collapse Approximation for Open-World Test-Time Adaptation
Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局前沿人工智能研究中心）；Sun Yat-sen University（中山大学）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Mathematics (Zhuhai), Sun Yat-sen University（中山大学（珠海校区）数学学院）
MixLoRA-DSI: Dynamically Expandable Mixture-of-LoRA Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora
Department of Data Science & AI, Monash University, Australia（数据科学与人工智能系，墨尔本大学，澳大利亚）；Department of Human Centred Computing, Monash University, Australia（以人为中心计算系，墨尔本大学，澳大利亚）
Dual Distribution Estimation for Zero-shot Noisy Test-Time Adaptation with VLMs
The Hong Kong Polytechnic University（香港理工大学）；Eastern Institute of Technology, Ningbo（宁波东方理工大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Shanghai Jiao Tong University（上海交通大学）
On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion
South China University of Technology（华南理工大学）；Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局（A*STAR）信息通信研究院（I2R））
Why Does RL Generalize Better Than SFT? A Data-Centric Perspective on VLM Post-Training
College of Computer Science, Sichuan University（四川大学计算机科学学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
OOD-HOI: Text-Driven 3D Whole-Body Human-Object Interactions Generation Beyond Training Domains
University of Chinese Academy of Sciences（中国科学院大学）；Shandong University（山东大学）；University of Science and Technology Beijing（北京科技大学）；Centre for Artificial Intelligence and Robotics, HKISI, CAS（中国科学院香港智能研究所人工智能与机器人中心）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Cross-Scenario Deraining Adaptation with Unpaired Data: Superpixel Structural Priors and Multi-Stage Pseudo-Rain Synthesis
School of Information Engineering, Guangdong University of Technology（广东工业大学信息工程学院）；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）
Unleashing the Potential of All Test Samples: Mean-Shift Guided Test-Time Adaptation
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（人机混合增强智能国家重点实验室，人工智能与机器人研究院，西安交通大学）；Faculty of Computility Microelectronics, Shenzhen University of Advanced Technology and the Guangdong Provincial Key Laboratory of Computility Microelectronics（计算微电子学系，深圳先进技术大学及广东省计算微电子重点实验室）；College of Software Engineering, Xi’an Jiaotong University（软件工程学院，西安交通大学）
