Temporal Dynamic Concept Modeling Network for Explainable Video Event Recognition

doi:10.1145/3568312

	Temporal Dynamic Concept Modeling Network for Explainable Video Event Recognition
	Zhang, Weigang 1; Qi, Zhaobo 1; Wang, Shuhui 2,3; Su, Chi 4; Su, Li 5; Huang, Qingming 6,7
	2023-11-01
发表期刊	ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
ISSN	1551-6857
卷号	19 期号:6 页码:22
摘要	Recently, with the vigorous development of deep learning and multimedia technology, intelligent urban computing has received more and more extensive attention from academia and industry. Unfortunately, most of the related technologies are black-box paradigms that lack interpretability. Among them, video event recognition is a basic technology. Event contains multiple concepts and their rich interactions, which can assist us to construct explainable event recognition methods. However, the crucial concepts needed to recognize events have various temporal existing patterns, and the relationship between events and the temporal characteristics of concepts has not been fully exploited. This brings great challenges for concept-based event categorization. To address the above issues, we introduce the temporal concept receptive field, which is the length of the temporal window size required to capture key concepts for concept-based event recognition methods. Accordingly, we introduce the temporal dynamic convolution (TDC) to model the temporal concept receptive field dynamically according to different events. Its core idea is to combine the results of multiple convolution layers with the learned coefficients from two complementary perspectives. These convolution layers contain a variety of kernel sizes, which can provide temporal concept receptive fields of different lengths. Similarly, we also propose the cross-domain temporal dynamic convolution (CrTDC) with the help of the rich relationship between different concepts. Different coefficients can help us to capture suitable temporal concept receptive field sizes and highlight crucial concepts to obtain accurate and complete concept representations for event analysis. Based on the TDC and CrTDC, we introduce the temporal dynamic concept modeling network (TDCMN) for explainable video event recognition. We evaluate TDCMN on large-scale and challenging datasets FCVID, ActivityNet, and CCV. Experimental results show that TDCMN significantly improves the event recognition performance of concept-based methods, and the explainability of our method inspires us to construct more explainable models from the perspective of the temporal concept receptive field.
关键词	Event recognition temporal concept receptive field dynamic convolution
DOI	10.1145/3568312
收录类别	SCI
语种	英语
资助项目	Technology and Innovation Major Project of the Ministry of Science and Technology of China[2020AAA0108400] ; Technology and Innovation Major Project of the Ministry of Science and Technology of China[2020AAA0108402] ; National Natural Science Foundation of China[61976069] ; National Natural Science Foundation of China[U21B2038] ; National Natural Science Foundation of China[62236008] ; National Natural Science Foundation of China[62022083] ; National Natural Science Foundation of China[61836002] ; National Natural Science Foundation of China[61931008] ; Beijing Nova Program[Z201100006820023] ; Fundamental Research Funds for the Central Universities
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:001035785200041
出版者	ASSOC COMPUTING MACHINERY
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21364
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Qi, Zhaobo; Huang, Qingming
作者单位	1.Harbin Inst Technol, Weihai 264209, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Peng Cheng Lab, Beijing 100190, Peoples R China 4.SmartMore, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Beijing 101478, Peoples R China 6.Univ Chinese Acad Sci, Inst Comp Technol, Chinese Acad Sci, Beijing 101478, Peoples R China 7.Peng Cheng Lab, Beijing 101478, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Weigang,Qi, Zhaobo,Wang, Shuhui,et al. Temporal Dynamic Concept Modeling Network for Explainable Video Event Recognition[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2023,19(6):22.
APA	Zhang, Weigang,Qi, Zhaobo,Wang, Shuhui,Su, Chi,Su, Li,&Huang, Qingming.(2023).Temporal Dynamic Concept Modeling Network for Explainable Video Event Recognition.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,19(6),22.
MLA	Zhang, Weigang,et al."Temporal Dynamic Concept Modeling Network for Explainable Video Event Recognition".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 19.6(2023):22.