Real-world multimodal knowledge graphs (MKGs) are inherently heterogeneous, modeling entities that are associated with diverse modalities. Traditional knowledge graph embedding (KGE) methods excel at learning continuous representations of entities and relations, yet they are typically designed for unimodal settings. Recent approaches extend KGE to multimodal settings but remain constrained, often processing modalities in isolation, resulting in weak cross-modal alignment, and relying on simplistic assumptions such as uniform modality availability across entities. Vision-Language Models (VLMs) offer a powerful way to align diverse modalities within a shared embedding space. We propose Vision-Language Knowledge Graph Embeddings (VL-KGE), a framework that integrates cross-modal alignment from VLMs with structured relational modeling to learn unified multimodal representations of knowledge graphs. Experiments on WN9-IMG and two novel fine art MKGs, WikiArt-MKG-v1 and WikiArt-MKG-v2, demonstrate that VL-KGE consistently improves over traditional unimodal and multimodal KGE methods in link prediction tasks. Our results highlight the value of VLMs for multimodal KGE, enabling more robust and structured reasoning over large-scale heterogeneous knowledge graphs.
When Vision Meets Graphs: A Survey on Graph Reasoning and Learning
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of Waterloo（滑铁卢大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Oxford（牛津大学）
More Than Meets the Eye? Uncovering the Reasoning-Planning Disconnect in Training Vision-Language Driving Models
S-Lab, Nanyang Technological University, Singapore（南洋理工大学S实验室）；College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算与数据科学学院）
When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Glasgow（格拉斯哥大学）；Boston University（波士顿大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence System
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学电子信息工程学院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；School of Earth Sciences and Engineering, Nanjing University（南京大学地球科学与工程学院）；Huawei Technologies Co., Ltd（华为技术有限公司）
When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review
King’s College London（伦敦国王学院）；University of Luxembourg（卢森堡大学）；Tongji University（同济大学）；Shanghai Institute of Intelligent Science and Technology, Tongji University（同济大学上海智能科学与技术研究院）
Vision-Language Meets the Skeleton: Progressively Distillation with Cross-Modal Knowledge for 3D Action Representation Learning
School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）；School of Automation Engineering, University of Electronic Science and Technology of China（电子科技大学自动化工程学院）；The Hong Kong Polytechnic University（香港理工大学）；Hong Kong Polytechnic University Shenzhen Research Institute（香港理工大学深圳研究院）；No.1 Orthopedic Hospital of Chengdu（成都市第一骨科医院）
Shanghai Jiao Tong University（上海交通大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；CloudWalk Technology Co., Ltd（云walk科技有限公司）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
MetaFruit Meets Foundation Models: Leveraging a Comprehensive Multi-Fruit Dataset for Advancing Agricultural Foundation Models
Michigan State University（密歇根州立大学）；Nanyang Technological University（南洋理工大学）；Shanghai Jiao Tong University（上海交通大学）；Pennsylvania State University（宾夕法尼亚州立大学）；United States Department of Agriculture Agricultural Research Service（美国农业部农业研究服务局）
