Knowledge Commons of Institute of Automation,CAS
Refinement of Boundary Regression Using Uncertainty in Temporal Action Localization | |
Chen YZ(陈云泽)1,2![]() ![]() ![]() ![]() ![]() | |
2020 | |
会议名称 | 2020 Proceedings of the British Machine Vision Conference |
会议日期 | 2020/07/09 |
会议地点 | virtual conference |
摘要 | Boundary localization is a key component of most temporal action localization frameworks for untrimmed video. Deep-learning methods have brought remarkable progress in this field due to large-scale annotated datasets (e.g., THUMOS14 and ActivityNet). However, natural ambiguity exists for labeling an accurate action boundaries with such datasets. In this paper, we propose a method to model this uncertainty. Specifically, we construct a Gaussian model for predicting the uncertainty variance of the boundary. The captured variance is further used to select more reliable proposals and to refine proposal boundaries by variance voting during post-processing. For most existing one- and two-stage frameworks, more accurate boundaries and reliable proposals can be obtained without additional computation. For the one-stage decoupled single-shot temporal action detection (Decouple-SSAD) framework, we first apply the adaptive pyramid feature fusion method to fuse its features of different scales and optimize its structure. Then, we introduce the uncertainty based method and improve state-of-the-art mAP@0.5 value from 37.9% to 41.6% on THUMOS14. Moreover, for the two-stage proposal–proposal interaction through a graph convolutional network (P-GCN), with such uncertainty method, we also gain significant improvements on both THUMOS14 and ActivityNet v1.3 datasets. |
语种 | 英语 |
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 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52384 |
专题 | 中国科学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.Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences Beijing, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences Beijing, China 3.Horizon Robotics 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,Mengjuan Chen,Rui Wu,et al. Refinement of Boundary Regression Using Uncertainty in Temporal Action Localization[C],2020. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Refinement of Bounda(3377KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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