A Multi-modal Global Instance Tracking Benchmark (MGIT): Better Locating Target in Complex Spatio-temporal and causal Relationship | |
Shiyu, Hu1,2; Dailing, Zhang1,2; Meiqi, Wu3; Xiaokun, Feng1,2; Xuchen, Li4; Xin, Zhao1,2; Kaiqi, Huang1,2,5 | |
2023-12 | |
会议日期 | 2023-12 |
会议地点 | New Orleans |
英文摘要 | Tracking an arbitrary moving target in a video sequence is the foundation for high-level tasks like video understanding. Although existing visual-based trackers have demonstrated good tracking capabilities in short video sequences, they always perform poorly in complex environments, as represented by the recently proposed global instance tracking task, which consists of longer videos with more complicated narrative content. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/54537] |
专题 | 智能系统与工程 |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences 2.Institute of Automation, Chinese Academy of Sciences 3.School of Computer Science and Technology, University of Chinese Academy of Sciences 4.School of Computer Science, Beijing University of Posts and Telecommunications 5.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Shiyu, Hu,Dailing, Zhang,Meiqi, Wu,et al. A Multi-modal Global Instance Tracking Benchmark (MGIT): Better Locating Target in Complex Spatio-temporal and causal Relationship[C]. 见:. New Orleans. 2023-12. |
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