Knowledge Commons of Institute of Automation,CAS
Temporal Action Detection with Dynamic Weights Based on Curriculum Learning | |
Chen YZ(陈云泽)1,2![]() ![]() ![]() ![]() ![]() | |
发表期刊 | Neurocomputing
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2023 | |
页码 | 106-116 |
摘要 | To enable temporal action localization, the computer needs to recognize the locations and classes of action instances in a video. The main challenge to temporal action detection is that the videos are often long and untrimmed, consisting of varying action content. Existing temporal action detection frameworks exhibit a gap between the training and testing phases, which is detrimental to model performance. Specifically, all positive samples are trained identically in the training phase. By contrast, in the testing phase, the positive samples with the best classification and localization scores are selected, while all others are suppressed. To mitigate this issue, we build an auxiliary branch to unify the training and testing procedures. In the construction of the auxiliary branch, we design a dynamic weighting strategy based on curriculum learning, where the weights of training samples are a combination of their classification and localization scores. Motivated by the speculation of curriculum learning, we emphasize the importance of classification and localization scores in different training stages. The classification score accounts for a higher proportion of the combined score in the early stages of the training process. As the epoch increases, the localization score gradually increases in proportion as well. The experimental results demonstrate that our methodology of curriculum-based learning enhances the performance of current action localization techniques. On THUMOS14, our technique outperforms the existing state of-the-art technique (57.6% vs 55.5%). And the performance on ActivityNet v1.3 (mAP@Avg) reaches 35.4%. |
收录类别 | SCI |
语种 | 英语 |
WOS记录号 | WOS:000990128300001 |
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/173211/52385 |
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 |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences, East Zhongguancun Road, Haidian District, Beijing, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Jingjia Road, Huairou District, Beijing, China |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Chen YZ,He jiang,Junrui Xiao,et al. Temporal Action Detection with Dynamic Weights Based on Curriculum Learning[J]. Neurocomputing,2023:106-116. |
APA | Chen YZ,He jiang,Junrui Xiao,Ding Li,&Qingyi Gu.(2023).Temporal Action Detection with Dynamic Weights Based on Curriculum Learning.Neurocomputing,106-116. |
MLA | Chen YZ,et al."Temporal Action Detection with Dynamic Weights Based on Curriculum Learning".Neurocomputing (2023):106-116. |
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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 | ||
Temporal action dete(1252KB) | 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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