We propose the ambiguity problem for the foreground object segmentation task and motivate the importance of estimating and accounting for this ambiguity when designing vision systems. Specifically, we distinguish between images which lead multiple annotators to segment different foreground objects (ambiguous) versus minor inter-annotator differences of the same object. Taking images from eight widely used datasets, we crowdsource labeling the images as "ambiguous" or "not ambiguous" to segment in order to construct a new dataset we call STATIC. Using STATIC, we develop a system that automatically predicts which images are ambiguous. Experiments demonstrate the advantage of our prediction system over existing saliency-based methods on images from vision benchmarks and images taken by blind people who are trying to recognize objects in their environment. Finally, we introduce a crowdsourcing system to achieve cost savings for collecting the diversity of all valid "ground truth" foreground object segmentations by collecting extra segmentations only when ambiguity is expected. Experiments show our system eliminates up to 47% of human effort compared to existing crowdsourcing methods with no loss in capturing the diversity of ground truths.
Relevant Intrinsic Feature Enhancement Network for Few-Shot Semantic Segmentation
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Alibaba group（阿里巴巴集团）
OMUDA: Omni-level Masking for Unsupervised Domain Adaptation in Semantic Segmentation
School of Mechanical Engineering, Chengdu University（成都大学机械工程学院）；School of Information Science and Technology, Harbin Institute of Technology（哈尔滨工业大学信息科学与技术学院）；School of Computer Science and Informatics, Cardiff University（卡的夫大学计算机科学与信息学院）；School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机科学学院）；College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学与技术学院）
AgMTR: Agent Mining Transformer for Few-shot Segmentation in Remote Sensing
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Electronics, Chinese Academy of Sciences（中国科学院电子学研究所）；Tsinghua University（清华大学）
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能研究院）
Segment-to-Act: Label-Noise-Robust Action-Prompted Video Segmentation Towards Embodied Intelligence
School of Artificial Intelligence and Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, China（人工智能与机器人学院及机器人视觉感知与控制技术国家工程研究中心，湖南大学，中国）；Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Germany（人机学与机器人研究所，卡尔斯鲁厄理工学院，德国）
MS-DKC: A Dataset Knowledge Card Framework for Designing and Adapting Medical Image Segmentation Models
Center of Excellence in Precision Medicine and Digital Health, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand（精准医学与数字健康中心，朱拉隆功大学牙科学院，泰国曼谷）；Department of Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan（计算机工程系，COMSATS伊斯兰堡大学，巴基斯坦伊斯兰堡）；School of Biomedical Engineering, UNSW, Sydney, NSW, Australia（生物医学工程学院，新南威尔士大学，澳大利亚悉尼，新南威尔士）；Visiting Scholar (Collaborative Projects), Center of Excellence in Precision Medicine and Digital Health, Chulalongkorn University, Bangkok, Thailand（访问学者（合作项目），精准医学与数字健康中心，朱拉隆功大学，泰国曼谷）；Department of Computing, Abasyn University Islamabad Campus (AUIC), Islamabad, Pakistan（计算系，阿巴斯扬大学伊斯兰堡校区（AUIC），巴基斯坦伊斯兰堡）；Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates（Mohamed bin Zayed人工智能大学，阿布扎比，阿拉伯联合酋长国）；College of Computer and Information Sciences, prince Sultan University, Riyadh, SAudi Arabia（计算机与信息科学学院，苏丹王子大学，沙特阿拉伯利雅得）
