Grounding textual expressions on scene objects from first-person views is a truly demanding capability in developing agents that are aware of their surroundings and behave following intuitive text instructions. Such capability is of necessity for glass-devices or autonomous robots to localize referred objects in the real-world. In the conventional referring expression comprehension tasks of images, however, datasets are mostly constructed based on the web-crawled data and don't reflect diverse real-world structures on the task of grounding textual expressions in diverse objects in the real world. Recently, a massive-scale egocentric video dataset of Ego4D was proposed. Ego4D covers around the world diverse real-world scenes including numerous indoor and outdoor situations such as shopping, cooking, walking, talking, manufacturing, etc. Based on egocentric videos of Ego4D, we constructed a broad coverage of the video-based referring expression comprehension dataset: RefEgo. Our dataset includes more than 12k video clips and 41 hours for video-based referring expression comprehension annotation. In experiments, we combine the state-of-the-art 2D referring expression comprehension models with the object tracking algorithm, achieving the video-wise referred object tracking even in difficult conditions: the referred object becomes out-of-frame in the middle of the video or multiple similar objects are presented in the video. Codes are available at https://github.com/shuheikurita/RefEgo
The University of Hong Kong（香港大学）；University of Wollongong（伍伦贡大学）；Tencent AI Lab（腾讯人工智能实验室）；Australian Centre for Robotic Vision, University of Adelaide（阿德莱德大学澳大利亚机器人视觉中心）
Northwestern Polytechnical University（西北工业大学）；National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology（空天地海一体化大数据应用技术国家工程实验室）
Referring Expression Comprehension Using Language Adaptive Inference
College of Computer Science & Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；Shanghai Institute for Advanced Study, Zhejiang University（浙江大学上海高等研究院）；Shanghai AI Laboratory（上海人工智能实验室）
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）
The University of Western Australia（西澳大利亚大学）；Murdoch University（莫道克大学）；Sun Yat-sen University（中山大学）；The Hong Kong University of Science and Technology（香港科技大学）
ScanFormer: Referring Expression Comprehension by Iteratively Scanning
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Software Technology, Zhejiang University（浙江大学软件学院）；Zhejiang-Singapore Innovation and Al Joint Research Lab（浙新创新与人工智能联合研究实验室）
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）
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）
