ReCo-Diff: Residual-Conditioned Deterministic Sampling for Cold Diffusion in Sparse-View CT
National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea（韩国全南数学研究所）；Department of Computer Engineering, Korea National University of Transportation, Chungju, 27469, Republic of Korea（韩国交通国立大学计算机工程系）
Yong Eun Choi, Hyoung Suk Park, Kiwan Jeon, Hyun-Cheol Park, Sung Ho Kang
Cold and generalized diffusion models have recently shown strong potential for sparse-view CT reconstruction by explicitly modeling deterministic degradation processes. However, existing sampling strategies often rely on ad hoc sampling controls or fixed schedules, which remain sensitive to error accumulation and sampling instability. We propose ReCo-Diff, a residual-conditioned diffusion framework that leverages observation residuals through residual-conditioned self-guided sampling. At each sampling step, ReCo-Diff first produces a null (unconditioned) baseline reconstruction and then conditions subsequent predictions on the observation residual between the predicted image and the measured sparse-view input. This residual-driven guidance provides continuous, measurement-aware correction while preserving a deterministic sampling schedule, without requiring heuristic interventions. Experimental results demonstrate that ReCo-Diff consistently outperforms existing cold diffusion sampling baselines, achieving higher reconstruction accuracy, improved stability, and enhanced robustness under severe sparsity.
Image-Conditioned Diffusion Models for Quality Assurance of Organ-at-Risk Segmentations in Radiotherapy
UCL Hawkes Institute（UCL霍克斯研究所）；University College London（伦敦大学学院）；National Physical Laboratory（英国国家物理实验室）；University College London Hospital（伦敦大学学院医院）；National Radiotherapy Trials Quality Assurance Group（国家放射治疗试验质量保证组）；Mount Vernon Hospital（芒特弗农医院）；University of Manchester（曼彻斯特大学）
Toward High-Fidelity Visual Reconstruction: From EEG-Based Conditioned Generation to Joint-Modal Guided Rebuilding
Beijing University of Technology, Beijing（北京理工大学）；Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing（北京计算智能与智能系统重点实验室）
MCAD: Multi-modal Conditioned Adversarial Diffusion Model for High-Quality PET Image Reconstruction
School of Computer Science, Sichuan University（四川大学计算机学院）；The Hong Kong Polytechnic University（香港理工大学）；School of Computer Science, Chengdu University of Information Technology（成都信息工程大学计算机学院）
Cross-Modal Conditioned Reconstruction for Language-guided Medical Image Segmentation
College of Information and Electrical Engineering, China Agricultural University（中国农业大学信息与电气工程学院）；Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, China Agricultural University（中国农业大学北京农业物联网工程技术研究中心）；School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；School of Engineering, The Hong Kong University of Science and Technology（香港科技大学工程学院）；Department of Electrical Engineering, Yale University（耶鲁大学电气工程系）
Geo-EVS: Geometry-Conditioned Extrapolative View Synthesis for Autonomous Driving
School of Vehicle and Mobility, Tsinghua University（清华大学车辆与运载学院）；State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University（清华大学智能绿色车辆与移动国家重点实验室）；School of Science, Minzu University（中央民族大学理学院）；Chongqing Changan Automobile Co., Ltd.（重庆长安汽车股份有限公司）
Modeling 3D Surface Manifolds with a Locally Conditioned Atlas
Faculty of Mathematics and Computer Science, Jagiellonian University（雅盖隆大学数学与计算机科学学院）；IDEAS NCBR；Wroclaw University of Science and Technology（弗罗茨瓦夫理工大学）；Warsaw University of Technology（华沙理工大学）
MGCR-Net:Multimodal Graph-Conditioned Vision-Language Reconstruction Network for Remote Sensing Change Detection
School of Computer Science and Technology, Shandong Technology and Business University（山东科技职业大学计算机科学与技术学院）；School of Computer Science and Technology, Qingdao University（青岛大学计算机科学与技术学院）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）
CCAD: Compressed Global Feature Conditioned Anomaly Detection
Columbia University（哥伦比亚大学）；Ping An Property & Casualty Insurance Company（平安财产保险股份有限公司）；University of Michigan（密歇根大学）；University of California at Berkeley（加州大学伯克利分校）；Stevens Institute of Technology（史蒂文斯理工学院）；VisionX LLC.（VisionX有限责任公司）
EO-VGGT: Orbital Ray-Conditioned 3D Foundation Models for Satellite Multi-View Reconstruction
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；Hubei Luojia Laboratory（湖北珞珈实验室）
Brain-DiT: A Universal Multi-state fMRI Foundation Model with Metadata-Conditioned Pretraining
Department of Biomedical Engineering, Southern University of Science and Technology, China（南方科技大学生物医学工程系，中国）；School of Biomedical Engineering, Shenzhen University, China（深圳大学生物医学工程学院，中国）
