We propose a robust solution to future trajectory forecast, which can be practically applicable to autonomous agents in highly crowded environments. For this, three aspects are particularly addressed in this paper. First, we use composite fields to predict future locations of all road agents in a single-shot, which results in a constant time complexity, regardless of the number of agents in the scene. Second, interactions between agents are modeled as a non-local response, enabling spatial relationships between different locations to be captured temporally as well (i.e., in spatio-temporal interactions). Third, the semantic context of the scene are modeled and take into account the environmental constraints that potentially influence the future motion. To this end, we validate the robustness of the proposed approach using the ETH, UCY, and SDD datasets and highlight its practical functionality compared to the current state-of-the-art methods.
A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint
Istanbul Technical University（伊斯坦布尔技术大学）；University of Sousse（苏塞大学）；National School for Computer Science (ENSI)（国家计算机科学学校）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；ShanghaiTech University（上海科技大学）；United Imaging Intelligence Co., Ltd.（联影智能有限公司）
Prompting Decision Transformer for Few-Shot Policy Generalization
Carnegie Mellon University（卡内基梅隆大学）；University of Montreal（蒙特利尔大学）；Mila（米拉）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）；Massachusetts Institute of Technology（麻省理工学院）；UMass Amherst（马萨诸塞大学阿默斯特分校）
Schrödinger's Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation
Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai University of International Business and Economics（上海对外经贸大学）；Shanghai Innovation Institute（上海创新研究院）
Multi-Modal Zero-Shot Prediction of Color Trajectories in Food Drying
Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA（机械科学与工程系，伊利诺伊大学厄巴纳-香槟分校）；Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA（机械工程系，密歇根大学安娜堡分校）
How You Move Tells What You'll Do: Trajectory-Conditioned Egocentric Prediction
Khoury College of Computer Sciences, Northeastern University, Boston（北德文斯克学院，东北大学，波士顿）；Korea Advanced Institute of Science and Technology, Daejeon（韩国科学技术院，大田）；Department of Mathematics and Computer Science, University of Catania, Italy（卡塔尼亚大学数学与计算机科学系，意大利）
