Grounding referring expressions in images aims to locate the object instance in an image described by a referring expression. It involves a joint understanding of natural language and image content, and is essential for a range of visual tasks related to human-computer interaction. As a language-to-vision matching task, the core of this problem is to not only extract all the necessary information (i.e., objects and the relationships among them) in both the image and referring expression, but also make full use of context information to align cross-modal semantic concepts in the extracted information. Unfortunately, existing work on grounding referring expressions fails to accurately extract multi-order relationships from the referring expression and associate them with the objects and their related contexts in the image. In this paper, we propose a Cross-Modal Relationship Extractor (CMRE) to adaptively highlight objects and relationships (spatial and semantic relations) related to the given expression with a cross-modal attention mechanism, and represent the extracted information as a language-guided visual relation graph. In addition, we propose a Gated Graph Convolutional Network (GGCN) to compute multimodal semantic contexts by fusing information from different modes and propagating multimodal information in the structured relation graph. Experimental results on three common benchmark datasets show that our Cross-Modal Relationship Inference Network, which consists of CMRE and GGCN, significantly surpasses all existing state-of-the-art methods. Code is available at https://github.com/sibeiyang/sgmn/tree/master/lib/cmrin_models
Key Lab of Intell. Info. Process., Inst. of Comput. Tech., CAS（中国科学院计算技术研究所智能信息处理重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；University of Science and Technology of China（中国科学技术大学）；Peng Cheng Laboratory（鹏城实验室）
Hierarchical Alignment-enhanced Adaptive Grounding Network for Generalized Referring Expression Comprehension
School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）；Institute of Big Data, Fudan University（复旦大学大数据学院）；Shanghai University of Finance and Economics（上海财经大学）；Nanyang Technological University（南洋理工大学）
A-PAIR: A Benchmark and Identity-Consistent Grounding Framework for Air-Ground Cross-View Referring Person Detection
School of Automation, Southeast University（东南大学自动化学院）；Faculty of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；School of Sports Training, Tianjin University of Sport（天津体育学院运动训练学院）；Tianjin University（天津大学）；National University of Defense Technology（国防科技大学）
University of Chinese Academy of Sciences（中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）；University of Science and Technology of China（中国科学技术大学）；Nanjing University of Science and Technology（南京理工大学）；Huawei Cloud & AI（华为云与人工智能部门）
The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Southern University of Science and Technology（南方科技大学）
AugRefer: Advancing 3D Visual Grounding via Cross-Modal Augmentation and Spatial Relation-based Referring
University of Science and Technology of China（中国科学技术大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Hefei University of Technology（合肥工业大学）
MIMO: A medical vision language model with visual referring multimodal input and pixel grounding multimodal output
School of Software & Microelectronics, Peking University（软件与微电子学院，北京大学）；School of Computer Science, Peking University（计算机学院，北京大学）；National Engineering Research Center for Software Engineering, Peking University（软件工程国家工程研究中心，北京大学）；Peking University Sixth Hospital（北京大学第六医院）；Augusta University（奥古斯塔大学）；Peking University First Hospital（北京大学第一医院）
