Know Who You Are: Learning Target-Aware Transformer for Object Tracking | |
Zhuojun Zou1,2![]() ![]() ![]() | |
2023-07 | |
会议名称 | 2023 IEEE International Conference on Multimedia and Expo (ICME) |
会议日期 | 10-14 July 2023 |
会议地点 | Brisbane, Australia |
摘要 | Tracking methods for measuring the similarity between the template and search region have achieved great success in recent years. Although many researchers have made efforts to introduce template annotations into network, inductive bias for trackers is unavoidable due to the inherent disadvantage of box representation. In this work, a novel tracking framework is proposed to eliminate the misguidance of biased prior, based on which, a target-aware Transformer tracker is designed. We use the template annotation as a predicted item in supervised learning, train our model to estimate the same target in template and search frame simultaneously, so that the tracker can learn the target-awareness both in the past and present frame. Our method can be assembled on the vast majority of Transformerbased networks. Sufficient experiments on six datasets verify the correctness of the proposed model. Without the bells and whistles, our tracker achieves the state-of-the-art performance on multiple benchmarks. |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52278 |
专题 | 国家专用集成电路设计工程技术研究中心_实感计算 |
通讯作者 | Jie Hao |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Guangdong Institute of Artificial Intelligence and Advanced Computing |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhuojun Zou,Xuexin Liu,Yuanpei Zhang,et al. Know Who You Are: Learning Target-Aware Transformer for Object Tracking[C],2023. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
2023106838.pdf(3023KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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