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Temporal Action Proposal Generation With Action Frequency Adaptive Network
Yepeng Tang2; Weining Wang1; Chunjie Zhang2; Jing Liu1; Yao Zhao2
发表期刊IEEE Transactions on Multimedia
ISSN1520-9210
2023-07
卷号26页码:2340 - 2353
通讯作者Zhang, Chunjie(cjzhang@bjtu.edu.cn)
文章类型SCI
摘要

As the cornerstone of human-behavior analysis in video understanding, temporal action proposal generation aims to predict the starting and ending time of human action instances in untrimmed videos. Although large achievements in temporal action proposal generation have been achieved, most previous studies ignore the variability of action frequency in raw videos, leading to unsatisfying performances on high-action-frequency videos. In fact, there exists two main issues which should be well addressed: data imbalance between high and low action-frequency videos, and inferior detection of short actions in high-action-frequency videos. To address the above issues, we propose an effective framework by adapting to the variability of action frequency, namely Action Frequency Adaptive Network (AFAN), which can be flexibly built upon any temporal action proposal generation method. AFAN consists of two modules: Learning From Experts (LFE) and Fine-Grained Processing (FGP). The LFE first trains a series of action proposal generators on different subsets of imbalanced data as experts and then teaches a unified student model via knowledge distillation. To better detect short actions, FGP first finds out high-action-frequency videos and then performs fine-grained detection. Extensive experimental results on four benchmark datasets (ActivityNet-1.3, HACS, THUMOS14 and FineAction) demonstrate the effectiveness and generalizability of the proposed AFAN, especially for high-action-frequency videos.

关键词Proposals Task analysis Data models Time-frequency analysis Representation learning Predictive models Information science Temporal action proposal generation expert learning fine-gained detection action frequency
DOI10.1109/TMM.2023.3295090
关键词[WOS]IMBALANCED DATA ; SMOTE
收录类别SCI
语种英语
资助项目Institute of Automation, Chinese Academy of Sciences
项目资助者Institute of Automation, Chinese Academy of Sciences
WOS研究方向Computer Science ; Telecommunications
WOS类目Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号WOS:001168330100010
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类多模态智能
国重实验室规划方向分类多模态协同认知
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被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/55386
专题紫东太初大模型研究中心
作者单位1.Institute of Automation, Chinese Academy of Sciences
2.h The Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University
推荐引用方式
GB/T 7714
Yepeng Tang,Weining Wang,Chunjie Zhang,et al. Temporal Action Proposal Generation With Action Frequency Adaptive Network[J]. IEEE Transactions on Multimedia,2023,26:2340 - 2353.
APA Yepeng Tang,Weining Wang,Chunjie Zhang,Jing Liu,&Yao Zhao.(2023).Temporal Action Proposal Generation With Action Frequency Adaptive Network.IEEE Transactions on Multimedia,26,2340 - 2353.
MLA Yepeng Tang,et al."Temporal Action Proposal Generation With Action Frequency Adaptive Network".IEEE Transactions on Multimedia 26(2023):2340 - 2353.
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