本文介绍了SemiRES，这是一个半监督框架，能够有效利用标注数据和未标注数据的组合来执行RES。将半监督技术应用于RES的一个重大障碍是存在大量噪声伪标签，尤其是在物体边界处。SemiRES集成了以精确边界划分而闻名的Segment Anything Model (SAM)，以提高这些伪标签的准确性。在SemiRES中，我们提供了两种可选的匹配策略：基于IoU的最优匹配(IOM)和复合部件整合(CPI)。这些策略旨在从SAM的输出中提取最准确的掩码，从而以更高的精度指导学生模型的训练。在无法从可用候选中匹配到精确掩码的情况下，我们开发了像素级调整(PWA)策略，直接通过伪标签指导学生模型的训练。在三个RES基准——RefCOCO、RefCOCO+和G-Ref上的大量实验表明，与全监督方法相比，其性能更优。值得注意的是，仅使用1%的标注数据，我们的SemiRES就大幅优于监督基线，例如在RefCOCO验证集上获得了+18.64%的增益。项目代码可在https://github.com/nini0919/SemiRES获取。
英文摘要：
In this paper, we introduce SemiRES, a semi-supervised framework that effectively leverages a combination of labeled and unlabeled data to perform RES. A significant hurdle in applying semi-supervised techniques to RES is the prevalence of noisy pseudo-labels, particularly at the boundaries of objects. SemiRES incorporates the Segment Anything Model (SAM), renowned for its precise boundary demarcation, to improve the accuracy of these pseudo-labels. Within SemiRES, we offer two alternative matching strategies: IoU-based Optimal Matching (IOM) and Composite Parts Integration (CPI). These strategies are designed to extract the most accurate masks from SAM's output, thus guiding the training of the student model with enhanced precision. In instances where a precise mask cannot be matched from the available candidates, we develop the Pixel-Wise Adjustment (PWA) strategy, guiding the student model's training directly by the pseudo-labels. Extensive experiments on three RES benchmarks--RefCOCO, RefCOCO+, and G-Ref reveal its superior performance compared to fully supervised methods. Remarkably, with only 1% labeled data, our SemiRES outperforms the supervised baseline by a large margin, e.g. +18.64% gains on RefCOCO val set. The project code is available at \url{https://github.com/nini0919/SemiRES}.
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）
SSP-SAM: SAM with Semantic-Spatial Prompt for Referring Expression Segmentation
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；NExT++ Lab, School of Computing, National University of Singapore（新加坡国立大学计算机学院NExT++实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation
College of Computer Science and Technology, National University of Defense Technology（国防科技大学计算机学院）；School of Computer Science, The University of Sydney（悉尼大学计算机学院）
Self-paced Multi-grained Cross-modal Interaction Modeling for Referring Expression Comprehension
School of Software Technology, Zhejiang University（浙江大学软件学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Zhejiang University-University of Illinois at Urbana-Champaign Institute, Zhejiang University（浙江大学-伊利诺伊大学厄巴纳-香槟分校研究院）
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）
Proxy-Embedding as an Adversarial Teacher: An Embedding-Guided Bidirectional Attack for Referring Expression Segmentation Models
National Pilot School of Software, Yunnan University（云南大学软件试点学校）；School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）
Meta Compositional Referring Expression Segmentation
Singapore University of Technology and Design（新加坡科技设计大学）；ByteDance（字节跳动）；Institute for Infocomm Research（资讯通信研究院）；Centre for Frontier AI Research（前沿人工智能研究中心）
Entity-enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding
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）
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）
A Unified Mutual Supervision Framework for Referring Expression Segmentation and Generation
The Chinese University of Hong Kong（香港中文大学）；International Digital Economy Academy (IDEA)（国际数字经济研究院（IDEA））；The Hong Kong University of Science and Technology（香港科技大学）；Tsinghua University（清华大学）
Griffon v2: Advancing Multimodal Perception with High-Resolution Scaling and Visual-Language Co-Referring
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Peng Cheng Laboratory, Shenzhen, China（鹏城实验室）；Wuhan AI Research, Wuhan, China（武汉人工智能研究所）
提出统一高分辨率通用模型 Griffon v2，通过轻量下采样投影器和即插即用视觉分词器实现视觉-语言共指代，在多项视觉感知任务上达到 SOTA 并超越专家模型。
Continual Referring Expression Comprehension via Dual Modular Memorization
University of Electronic Science and Technology of China（电子科技大学）；Peng Cheng Laboratory（鹏城实验室）；University of Wollongong（伍伦贡大学）；Hefei University of Technology（合肥工业大学）
Differentiated Relevances Embedding for Group-based Referring Expression Comprehension
The Hong Kong Polytechnic University（香港理工大学）；University of Glasgow（格拉斯哥大学）；Xiamen University（厦门大学）；Tsinghua University（清华大学）；Huawei Noahs Ark Lab（华为诺亚方舟实验室）；Fuzhou Institute for Data Technology（福州数据技术研究院）
LongEgoRefer: A Benchmark for Long-Form Egocentric Video Referring Expression Comprehension
Woven by Toyota, Japan（丰田公司，日本）；The University of Tokyo, Japan（东京大学，日本）；National Institute of Informatics, Japan（日本信息机构国家研究所）；Institute of Science Tokyo, Japan（东京科学研究所，日本）；NII LLMC, Japan（日本信息机构LLMC）；Kyoto University, Japan（京都大学，日本）
Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D Referring Expression Comprehension
Institute of Deep Perception Technology, JITRI（JITRI深度感知技术研究所）；University of Liverpool（利物浦大学）；University of Southampton（南安普顿大学）；Momoni AI（Momoni人工智能公司）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；Thrust of Artificial Intelligence, HKUST (GZ)（香港科技大学（广州）人工智能学域）；Thrust of Intelligent Transportation, HKUST (GZ)（香港科技大学（广州）智能交通学域）
REF-VLM: Triplet-Based Referring Paradigm for Unified Visual Decoding
School of Computer Science, Shanghai Jiao Tong University, Shanghai, 200240, China（上海交通大学计算机科学学院）；Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China（宁波数字孪生研究院）；School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China（上海交通大学信息科学与电子工程学院）
