Developing link prediction models to automatically complete knowledge graphs has recently been the focus of significant research interest. The current methods for the link prediction taskhavetwonaturalproblems:1)the relation distributions in KGs are usually unbalanced, and 2) there are many unseen relations that occur in practical situations. These two problems limit the training effectiveness and practical applications of the existing link prediction models. We advocate a holistic understanding of KGs and we propose in this work a unified Generalized Relation Learning framework GRL to address the above two problems, which can be plugged into existing link prediction models. GRL conducts a generalized relation learning, which is aware of semantic correlations between relations that serve as a bridge to connect semantically similar relations. After training with GRL, the closeness of semantically similar relations in vector space and the discrimination of dissimilar relations are improved. We perform comprehensive experiments on six benchmarks to demonstrate the superior capability of GRL in the link prediction task. In particular, GRL is found to enhance the existing link prediction models making them insensitive to unbalanced relation distributions and capable of learning unseen relations.
OC-SOP: Enhancing Vision-Based 3D Semantic Occupancy Prediction by Object-Centric Awareness
Autonomous Intelligent Systems group, Computer Science Institute VI – Intelligent Systems and Robotics – and the Center for Robotics and the Lamarr Institute for Machine Learning and Artificial Intelligence, University of Bonn, Germany（自主智能系统组，计算机科学研究所VI——智能系统与机器人——以及机器人中心和拉玛尔人工智能与机器学习研究所，波恩大学，德国）
Cyber Physical Awareness via Intent-Driven Threat Assessment: Enhanced Space Networks with Intershell Links
Department of Electronics and Communication Engineering, Istanbul Technical University（电子与通信工程系，伊斯坦布尔技术大学）；Department of Electrical Engineering, Poly-Grames Research Center, Polytechnique Montreal（电气工程系，Poly-Grames研究中心，蒙特利尔理工学院）；Department of Computer Engineering, Istanbul Technical University（计算机工程系，伊斯坦布尔技术大学）
BiCo-Fusion: Bidirectional Complementary LiDAR-Camera Fusion for Semantic- and Spatial-Aware 3D Object Detection
AI Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）人工智能方向）；School of Electrical and Electronic Engineering (EEE), Nanyang Technological University (NTU)（南洋理工大学电子与电气工程学院）
TGP: Two-modal occupancy prediction with 3D Gaussian and sparse points for 3D Environment Awareness
China Telecom Research Institute（中国电信研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）
ChangeQuery: Advancing Remote Sensing Change Analysis for Natural and Human-Induced Disasters from Visual Detection to Semantic Understanding
School of Computer Science and Technology and the Ministry of Education Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University（计算机科学与技术学院和教育部智能网络与网络安全重点实验室，西安交通大学）；State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences（遥感与数字地球国家重点实验室，航空信息研究所，中国科学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（测绘遥感信息工程国家重点实验室，武汉大学）；Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿-罗斯托克研究所）；Lancaster Environment Centre, Lancaster University（兰卡斯特大学环境研究中心）；Faculty of Electrical and Computer Engineering, University of Iceland（冰岛大学电气与计算机工程学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
