Online 3D scene graph generation builds a persistent, structured representation of a scene by incrementally fusing 2D observations into a global 3D graph. Existing online methods treat this fusion as a fully deterministic pipeline, where we identify three sources of uncertainty that are overlooked: observation, 2D model, and 3D representation. We propose PUF: a Plug-and-play, Uncertainty-aware, and training-free Fusion framework. Scene graph node association is reformulated as a probabilistic likelihood over semantic and spatial factors, replacing binary accept/reject gates. Dirichlet evidence accumulation distributes class and relationship evidence across plausible candidates proportional to association likelihood. An optional class-conditional prior completes edges for sparsely or never co-observed object pairs. We instantiate PUF with both a 3D Gaussian and a 3D voxel backend and observe consistent improvements, demonstrating its ability to generalize across different representations. Experiments on the 3DSSG and ReplicaSSG benchmarks show that our method substantially outperforms existing approaches while maintaining real-time latency. These results establish uncertainty-aware fusion as a principled and effective paradigm for online 3D scene understanding. The source code is publicly available at https://github.com/yyyyangyi/PUF.
Instituto de Telecomunicações（电信研究所）；Instituto Superior Técnico（高等技术学院）
The recently proposed plug-and-play (PnP) framework allows leveraging recent developments in image denoising to tackle other, more involved, imaging inverse problems. In a PnP method, a black-box denoiser is plugged into an iterative algorithm, taking the place of a formal denoising step that corresponds to the proximity operator of some convex regularizer. While this approach offers flexibility and excellent performance, convergence of the resulting algorithm may be hard to analyze, as most state-of-the-art denoisers lack an explicit underlying objective function. In this paper, we propose a …
P$^2$HCT: Plug-and-Play Hierarchical C2F Transformer for Multi-Scale Feature Fusion
Department of Automation Tsinghua University Beijing, China；Department of R\&D Linsulabs Chengdu, China；School of Information；Communication Engineering University of Electronic Science
RXFOOD: Plug-in RGB-X Fusion for Object of Interest Detection
Cleveland State University（克利夫兰州立大学）；Centre for Frontier AI Research (CFAR)（前沿人工智能研究中心）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Institute of High Performance Computing (IHPC)（高性能计算研究所）；Brookhaven National Laboratory（布鲁克海文国家实验室）
RC-BEVFusion: A Plug-In Module for Radar-Camera Bird's Eye View Feature Fusion
Stellantis, Opel Automobile GmbH（斯泰兰蒂斯集团欧宝汽车有限公司）；Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau（莱茵兰-普法尔茨凯泽斯劳滕-兰道工业大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）
MGML: A Plug-and-Play Meta-Guided Multi-Modal Learning Framework for Incomplete Multimodal Brain Tumor Segmentation
Nanchang University（南昌大学）；Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University（中山大学孙逸仙纪念医院）；Zhujiang Hospital, Southern Medical University（南方医科大学珠江医院）；University of California Los Angeles（加州大学洛杉矶分校）；Hubei University of Economics（湖北经济学院）；Xi'an Jiaotong Liverpool University（西交利物浦大学）；Southeast University（东南大学）
PI-H2T: Enhancing Long-Tailed Visual Recognition with Permutation-Invariant and Head-to-Tail Feature Fusion
Guangdong Provincial Key Laboratory of Visual Media and Multidimensional Intelligence, CSSE, Shenzhen University, China（广东省视觉媒体与多维智能重点实验室，计算机科学与电子技术学院，深圳大学，中国）；Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, China（福建省智慧城市感知与计算重点实验室，信息学院，厦门大学，中国）；Department of Computer Science, Hong Kong Baptist University, China（计算机科学系，香港 Baptist 大学，中国）；Inspur Smart City Technology Co., Ltd., China（Inspur 智能城市技术有限公司，中国）
An Effective UNet Using Feature Interaction and Fusion for Organ Segmentation in Medical Image
National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University（合成视觉基础科学国家重点实验室，计算机科学学院，四川大学）；College of Computer Science, Sichuan University（计算机科学学院，四川大学）；College of Software Engineering, Sichuan University（软件工程学院，四川大学）
本文提出含通道空间交互、通道注意力和多级融合三模块的 U 形网络，以增强预训练特征利用，并在多器官与心脏分割基准上取得最优精度且计算开销适中。
Diverse via bounded Agreement: Geometric Regularization for Multimodal Fusion
Department of Informatics University of Bern（伯尔尼大学信息学院）；College of Computing and Data Science Nanyang Technological University（南洋理工大学计算机与数据科学学院）；School of Software Engineering Xi’an Jiaotong University（西安交通大学软件工程学院）
Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation
University of Electronic Science and Technology of China（电子科技大学）；Hainan University（海南大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Southeast University（东南大学）；The University of Tokyo（东京大学）
