Knowledge Commons of Institute of Automation,CAS
Class-wise boundary regression by uncertainty in temporal action detection | |
Chen, Yunze1,2![]() ![]() ![]() | |
发表期刊 | IET IMAGE PROCESSING
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ISSN | 1751-9659 |
2022-08-04 | |
页码 | 9 |
摘要 | Temporal action detection is a crucial aspect of video understanding. It aims to classify the action as well as locate the start and end boundaries of the action in the untrimmed videos. As deep learning is frequently utilized, the accuracy of annotation is crucial to boundary localization. However, it is observed that some annotation instances are ambiguous and the ambiguity varies between categories. To solve the problem above, a Gaussian model is built to estimate the boundary uncertainty for each instance. Based on instance uncertainty, category uncertainty is applied to describe the uncertainty of each category. By combining instance and category uncertainty, the boundaries of the selected proposals are refined and the ranking of candidate proposals is adjusted. Furthermore, overcorrection is avoided for categories with a high level of uncertainty. With the uncertainty approach, state-of-the-art performance is achieved: 57.5% on THUMOS14 (mAP@0.5) and 35.4% on ActivityNet (mAP@Avg). |
DOI | 10.1049/ipr2.12599 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Scientific Instrument Developing Project of the Chinese Academy of Sciences[YJKYYQ20200045] |
项目资助者 | Scientific Instrument Developing Project of the Chinese Academy of Sciences |
WOS研究方向 | Computer Science ; Engineering ; Imaging Science & Photographic Technology |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:000836034700001 |
出版者 | WILEY |
七大方向——子方向分类 | 图像视频处理与分析 |
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 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49828 |
专题 | 中科院工业视觉智能装备工程实验室_精密感知与控制 |
通讯作者 | Gu, Qingyi |
作者单位 | 1.Chinese Acad Sci, Ctr Precis Sensing & Control, Inst Automat, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China |
推荐引用方式 GB/T 7714 | Chen, Yunze,Chen, Mengjuan,Gu, Qingyi. Class-wise boundary regression by uncertainty in temporal action detection[J]. IET IMAGE PROCESSING,2022:9. |
APA | Chen, Yunze,Chen, Mengjuan,&Gu, Qingyi.(2022).Class-wise boundary regression by uncertainty in temporal action detection.IET IMAGE PROCESSING,9. |
MLA | Chen, Yunze,et al."Class-wise boundary regression by uncertainty in temporal action detection".IET IMAGE PROCESSING (2022):9. |
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Class-wise boundary (1440KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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