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Hybrid-Attention Enhanced Two-Stream Fusion Network for Video Venue Prediction

文献类型：期刊论文

作者Zhang, Yanchao1,3; Min, Weiqing1,2; Nie, Liqiang3; Jiang, Shuqiang1,2
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2021
卷号23页码:2917-2929
ISSN号1520-9210
关键词Visualization Feature extraction Convolution Streaming media Object oriented modeling Three-dimensional displays Neural networks Feature extraction knowledge representation supervised learning video signal processing
DOI10.1109/TMM.2020.3019714
英文摘要Video venue category prediction has been drawing more attention in the multimedia community for various applications such as personalized location recommendation and video verification. Most of existing works resort to the information from either multiple modalities or other platforms for strengthening video representations. However, noisy acoustic information, sparse textual descriptions and incompatible cross-platform data could limit the performance gain and reduce the universality of the model. Therefore, we focus on discriminative visual feature extraction from videos by introducing a hybrid-attention structure. Particularly, we propose a novel Global-Local Attention Module (GLAM), which can be inserted to neural networks to generate enhanced visual features from video content. In GLAM, the Global Attention (GA) is used to catch contextual scene-oriented information via assigning channels with various weights while the Local Attention (LA) is employed to learn salient object-oriented features via allocating different weights for spatial regions. Moreover, GLAM can be extended to ones with multiple GAs and LAs for further visual enhancement. These two types of features respectively captured by GAs and LAs are integrated via convolution layers, and then delivered into convolutional Long Short-Term Memory (convLSTM) to generate spatial-temporal representations, constituting the content stream. In addition, video motions are explored to learn long-term movement variations, which also contributes to video venue prediction. The content and motion stream constitute our proposed Hybrid-Attention Enhanced Two-Stream Fusion Network (HA-TSFN). HA-TSFN finally merges the features from two streams for comprehensive representations. Extensive experiments demonstrate that our method achieves the state-of-the-art performance in the large-scale dataset Vine. The visualization also shows that the proposed GLAM can capture complementary scene-oriented and object-oriented visual features from videos. Our code is available at: https://github.com/zhangyanchao1014/HA-TSFN.
资助项目Shandong Provincial Key Research and Development Program[2019JZZY010118] ; National Natural Science Foundation of China[61972378] ; National Natural Science Foundation of China[61532018] ; National Natural Science Foundation of China[U1936203] ; National Natural Science Foundation of China[U19B2040] ; Shandong Provincial Natural Science Foundation[ZR2019JQ23] ; Innovation Teams in Colleges and Universities in Jinan[2018GXRC014]
WOS研究方向Computer Science ; Telecommunications
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000688215600030
源URL[http://119.78.100.204/handle/2XEOYT63/17100]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Min, Weiqing
作者单位1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Shandong Univ, Sch Comp Sci & Technol, Qingdao 266000, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Yanchao,Min, Weiqing,Nie, Liqiang,et al. Hybrid-Attention Enhanced Two-Stream Fusion Network for Video Venue Prediction[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2021,23:2917-2929.
APA Zhang, Yanchao,Min, Weiqing,Nie, Liqiang,&Jiang, Shuqiang.(2021).Hybrid-Attention Enhanced Two-Stream Fusion Network for Video Venue Prediction.IEEE TRANSACTIONS ON MULTIMEDIA,23,2917-2929.
MLA Zhang, Yanchao,et al."Hybrid-Attention Enhanced Two-Stream Fusion Network for Video Venue Prediction".IEEE TRANSACTIONS ON MULTIMEDIA 23(2021):2917-2929.

入库方式： OAI收割

来源：计算技术研究所

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