Novel view synthesis (NVS) in dynamic scenes faces persistent challenges in memory consumption, model complexity, training efficiency, and rendering quality. Offline methods offer high fidelity but suffer from high memory usage and limited scalability, while online approaches often trade quality for speed and compactness. We propose Continual Dynamic Neural Graphics Primitives (CD-NGP), a continual learning framework that reduces memory overhead and enhances scalability through parameter reuse. To avoid feature interference in dynamic scenes and improve rendering quality, our method combines spatial and temporal hash encodings, which compactly represent scene structures and motion patterns. We also introduce a new dataset comprising multi-view, long-duration ($>1200$ frames) videos with both rigid and non-rigid motion, which is not found in existing benchmarks. CD-NGP is evaluated on public datasets and our long video dataset, demonstrating superior scalability and reconstruction quality. It significantly reduces training memory usage to <14GB and requires only 0.4MB/frame in streaming bandwidth on DyNeRF -- substantially lower than most online baselines.
Adaptive Visual Scene Understanding: Incremental Scene Graph Generation
College of Computing and Data Science, Nanyang Technological University (NTU)（南洋理工大学计算与数据科学学院）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）
Beihang University（北京航空航天大学）；Shandong University（山东大学）；Beijing Technology and Business University（北京工商大学）；Jingdezhen Research Institute of Beihang University（北京航空航天大学景德镇研究院）；Shandong University of Science and Technology（山东科技大学）
Continual Hand-Eye Calibration for Open-world Robotic Manipulation
School of Automation Science and Engineering, South China University of Technology（华南理工大学自动化科学与工程学院）；State Key Laboratory of Robotics, the Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences（中国科学院机器人研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Unlocking Compositional Generalization in Continual Few-Shot Learning
Faculty of Information Technology, University of Science, Vietnam National University（信息科技学院，科学大学，越南国家大学）；Department of Computer Science, University of Warwick（计算机科学系，沃里克大学）
CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection
Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, School of Computer Science and Technology, Xidian University（协同智能系统重点实验室、教育部、计算机科学与技术学院、西安电子科技大学）；Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, School of Artificial Intelligence, Xidian University（智能感知与图像理解重点实验室、教育部、人工智能学院、西安电子科技大学）；Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University（协同智能系统重点实验室、教育部、西安电子科技大学）
Interdisciplinary Program in Artificial Intelligence, Seoul National University（人工智能交叉学科项目，首尔国立大学）；AI Institute for Seoul National University（首尔国立大学人工智能研究所）；SK Telecom（SK电信）；Korea Institute of Science and Technology（韩国科学技术院）
Learning on the Fly: Replay-Based Continual Object Perception for Indoor Drones
National University of Science and Technology Politehnica Bucharest, Romania（罗马尼亚布加勒斯特多项技术大学）；Institute of Mathematics ``Simion Stoilow'' of the Romanian Academy, Romania（罗马尼亚科学院模拟昂斯数学研究所）；NORCE Norwegian Research Center, Norway（挪威NORCE研究中心）
Bisecle: Binding and Separation in Continual Learning for Video Language Understanding
School of Computer Science University of New South Wales（计算机科学学院新南威尔士大学）；School of Computer Science and Engineering University of New South Wales（计算机科学与工程学院新南威尔士大学）；DEVCOM Army Research Laboratory（陆军研究实验室）
Dynamic Cross-Modal Prompt Generation for Multimodal Continual Instruction Tuning
School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）
Revisiting Distillation for Continual Learning on Visual Question Localized-Answering in Robotic Surgery
Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London（伦敦大学学院惠康/工程与物理科学研究委员会介入与外科科学中心（WEISS））；The Chinese University of Hong Kong (CUHK)（香港中文大学（CUHK））；Shun Hing Institute of Advanced Engineering, CUHK（香港中文大学信兴高等工程研究所）
Generating Continual Human Motion in Diverse 3D Scenes
University of Tübingen（蒂宾根大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Meta AI Research（元人工智能研究院）；University of California, Berkeley（加利福尼亚大学伯克利分校）
Reusable Architecture Growth for Continual Stereo Matching
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Centre for Artificial Intelligence and Robotics, HK Institute of Science & Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院人工智能与机器人中心）；University of Science and Technology Beijing（北京科技大学）
Arctique: An artificial histopathological dataset unifying realism and controllability for uncertainty quantification
Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association（亥姆霍兹联合会马克斯·德尔布吕克分子医学中心）；Helmholtz Imaging（亥姆霍兹成像中心）；Charité—Universitätsmedizin Berlin（柏林夏里特医学院）；Freie Universität Berlin（柏林自由大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；University of Potsdam（波茨坦大学）
