Knowledge Commons of Institute of Automation,CAS
Knowledge-driven Egocentric Multimodal Activity Recognition | |
Huang, Yi1,2,3![]() ![]() ![]() ![]() ![]() | |
发表期刊 | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
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ISSN | 1551-6857 |
2020-12-01 | |
卷号 | 16期号:4页码:21 |
摘要 | Recognizing activities from egocentric multimodal data collected by wearable cameras and sensors, is gaining interest, as multimodal methods always benefit from the complementarity of different modalities. However, since high-dimensional videos contain rich high-level semantic information while low-dimensional sensor signals describe simple motion patterns of the wearer, the large modality gap between the videos and the sensor signals raises a challenge for fusing the raw data. Moreover, the lack of large-scale egocentric multimodal datasets due to the cost of data collection and annotation processes makes another challenge for employing complex deep learning models. To jointly deal with the above two challenges, we propose a knowledge-driven multimodal activity recognition framework that exploits external knowledge to fuse multimodal data and reduce the dependence on large-scale training samples. Specifically, we design a dual-GCLSTM (Graph Convolutional LSTM) and a multi-layer GCN (Graph Convolutional Network) to collectively model the relations among activities and intermediate objects. The dual-GCLSTM is designed to fuse temporal multimodal features with top-down relation-aware guidance. In addition, we apply a co-attention mechanism to adaptively attend to the features of different modalities at different timesteps. The multi-layer GCN aims to learn relation-aware classifiers of activity categories. Experimental results on three publicly available egocentric multimodal datasets show the effectiveness of the proposed model. |
关键词 | Egocentric videos wearable sensors graph neural networks |
DOI | 10.1145/3409332 |
关键词[WOS] | 1ST-PERSON VISION ; VIDEOS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2018AAA0100604] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[62072455] ; National Natural Science Foundation of China[61702511] ; National Natural Science Foundation of China[61751211] ; National Natural Science Foundation of China[61620106003] ; National Natural Science Foundation of China[61532009] ; National Natural Science Foundation of China[U1836220] ; National Natural Science Foundation of China[U1705262] ; National Natural Science Foundation of China[61872424] ; Key Research Program of Frontier Sciences of CAS[QYZDJSSWJSC039] ; Research Program of National Laboratory of Pattern Recognition[Z-2018007] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences of CAS ; Research Program of National Laboratory of Pattern Recognition |
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:000614096700017 |
出版者 | ASSOC COMPUTING MACHINERY |
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 |
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/173211/42874 |
专题 | 多模态人工智能系统全国重点实验室_多媒体计算 |
通讯作者 | Huang, Yi |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 95 Zhongguancun East Rd, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, 95 Zhongguancun East Rd, Beijing, Peoples R China 3.Peng Cheng Lab, Shenzhen, Peoples R China 4.Beijing Jiaotong Univ, Sch Comp & Informat Technol, 506 Room 9 Teaching Bldg, Beijing, Peoples R China 5.Beijing Jiaotong Univ, Beijing Key Lab Traff Data Anal & Min, 506 Room 9 Teaching Bldg, Beijing, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Huang, Yi,Yang, Xiaoshan,Gao, Junyu,et al. Knowledge-driven Egocentric Multimodal Activity Recognition[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2020,16(4):21. |
APA | Huang, Yi,Yang, Xiaoshan,Gao, Junyu,Sang, Jitao,&Xu, Changsheng.(2020).Knowledge-driven Egocentric Multimodal Activity Recognition.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,16(4),21. |
MLA | Huang, Yi,et al."Knowledge-driven Egocentric Multimodal Activity Recognition".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 16.4(2020):21. |
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