Accurate prediction of others' trajectories is essential for autonomous driving. Trajectory prediction is challenging because it requires reasoning about agents' past movements, social interactions among varying numbers and kinds of agents, constraints from the scene context, and the stochasticity of human behavior. Our approach models these interactions and constraints jointly within a novel Multi-Agent Tensor Fusion (MATF) network. Specifically, the model encodes multiple agents' past trajectories and the scene context into a Multi-Agent Tensor, then applies convolutional fusion to capture multiagent interactions while retaining the spatial structure of agents and the scene context. The model decodes recurrently to multiple agents' future trajectories, using adversarial loss to learn stochastic predictions. Experiments on both highway driving and pedestrian crowd datasets show that the model achieves state-of-the-art prediction accuracy.
CoPAD : Multi-source Trajectory Fusion and Cooperative Trajectory Prediction with Anchor-oriented Decoder in V2X Scenarios
School of Automation, Southeast University, Nanjing, China（自动化学院，东南大学，南京，中国）；Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing, China（工程复杂系统测量与控制关键实验室，教育部，南京，中国）
Multi-scale Temporal Fusion Transformer for Incomplete Vehicle Trajectory Prediction
University of California, Los Angeles (UCLA)（加利福尼亚大学洛杉矶分校）；FHWA Center of Excellence on New Mobility and Automated Vehicles（联邦公路管理局新 mobility 与自动驾驶汽车卓越中心）
MoE-Enhanced Multi-Domain Feature Selection and Fusion for Fast Map-Free Trajectory Prediction
School of Mechanical Engineering, Zhejiang University（浙江大学机械工程学院）；Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, School of Automation and Intelligent Manufacturing, Southern University of Science and Technology（南方科技大学自动化与智能制造学院）；Leapmotor Technology（Leapmotor科技）；Department of Aeronautical and Automotive Engineering, Loughborough University（伦敦大学洛桑学院航空与汽车工程系）
Out-of-Sight Embodied Agents: Multimodal Tracking, Sensor Fusion, and Trajectory Forecasting
Department of Electrical and Computer Engineering, Northeastern University（东北大学电气与计算机工程系）；Khoury College of Computer Sciences, Northeastern University（东北大学库里计算机科学学院）
GEOcc: Geometrically Enhanced 3D Occupancy Network with Implicit-Explicit Depth Fusion and Contextual Self-Supervision
School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；School of Electronics, Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Transportation and Traffic Engineering, Central South University（中南大学交通运输工程学院）
TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations
Perception, Robotics and Intelligent Machines Research Group (PRIME) Department of Computer Science, Université de Moncton, Canada（感知、机器人与智能机器研究组（PRIME）计算机科学系，新不伦瑞克大学）
Self-Growing Spatial Graph Network for Context-Aware Pedestrian Trajectory Prediction
Nanyang Technological University（南洋理工大学）
行人轨迹预测是一个活跃的研究领域，最近的工作是将行人社会互动及其上下文顺应性的准确模型嵌入到动态空间图中。然而，现有的作品依赖于空间的场景和动态的假设，这需要一个重大的挑战，以适应在未知的环境中的在线系统的图形结构。此外，还缺乏评估方法的关系建模预测性能的影响。为了填补这一空白，我们提出了社会轨迹推荐门控图递归邻域网络（STR-GGRNN），它使用基于上下文场景特征和行人视觉线索的数据驱动的自适应在线邻域推荐。通过在线非负矩阵分解（NMF）来构造用于预测行人轨迹的图邻接矩阵，从而实现邻域推荐。在ETH-UCY数据集上的实验结果表明，该方法的性能优于现有方法，最佳模型在ETH-UCY数据集上达到了12 cm ADE和$\sim$ 15 cm FDE。当每帧采样总共20 K个未来轨迹时，所提出的方法仅需要0.49秒。
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation
Université de technologie de Compiègne（贡比涅技术大学）；CNRS（法国国家科学研究中心）；Université de Rouen Normandie（鲁昂诺曼底大学）；Centre Henri Becquerel（亨利·贝克勒尔中心）；Institut universitaire de France（法国大学研究院）
Single Stage Warped Cloth Learning and Semantic-Contextual Attention Feature Fusion for Virtual TryOn
Bharti School of Telecommunications, Indian Institute of Technology, Delhi, India（印度德里印度理工学院巴蒂电信学院）；Indian Institute of Information Technology, Sonepat, Haryana, India（印度哈里亚纳邦索内帕特印度信息技术学院）；Department of Electrical Engineering, Indian Institute of Technology, Delhi, India（印度德里印度理工学院电气工程系）
Evidence fusion with contextual discounting for multi-modality medical image segmentation
Heudiasyc（Heudiasyc实验室）；CNRS（法国国家科学研究中心）；Université de technologie de Compiègne（贡比涅技术大学）；Quantif（Quantif团队）；LITIS（LITIS实验室）；University of Rouen Normandy（鲁昂诺曼底大学）；Institut universitaire de France（法国大学研究院）；Henri Becquerel Cancer Center（亨利·贝克勒尔癌症中心）
EgoRefine: Ego-Referenced Predictive Alignment and Trajectory-Conditioned Reliability-Aware Fusion for Asynchronous Collaborative Perception
Hunan University（湖南大学）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（湖南大学机器人视觉感知与控制技术国家工程研究中心）；Nanjing University of Posts and Telecommunications（南京邮电大学）
Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion for Vessel Traffic Surveillance in Inland Waterways
Wuhan University of Technology（武汉理工大学）；National Engineering Research Center for Water Transport Safety（国家水运安全工程技术研究中心）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Enhancing Visual In-Context Learning by Multi-Faceted Fusion
College of Intelligent Robotics and Advance Manufacturing, Fudan University（智能机器人与先进制造学院，复旦大学）；School of Microelectronics, Fudan University（微电子学院，复旦大学）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；School of Information Science and Engineering, East China University of Science and Technology（信息科学与工程学院，东华大学）
Human Pose Estimation with Spatial Contextual Information
Baidu Research, Baidu Inc.（百度研究部，百度公司）；Hong Kong University of Science and Technology（香港科技大学）；YouTu Lab, Tencent（优图实验室，腾讯）；The Chinese University of Hong Kong（香港中文大学）；University of Kentucky（肯塔基大学）
Context Patch Fusion With Class Token Enhancement for Weakly Supervised Semantic Segmentation
School of Cyber Science and Engineering, Wuxi University（网络安全科学与工程学院，无锡大学）；School of Informatics, Xiamen University（信息学院，厦门大学）；School of Computer Science, University of Liverpool（计算机科学学院，利物浦大学）
