Multi-scale Spatiotemporal Information Fusion Network for Video Action Recognition


Yang, John See Su and Cai, Yutong and Lin, Weiyao and Cheng, Ming-Ming and Liu, Guangcan and Xiong, Hongkai (2019) Multi-scale Spatiotemporal Information Fusion Network for Video Action Recognition. In: 33rd IEEE International Conference on Visual Communications and Image Processing, VCIP 2018, 9 -12 December 2018, Taichung, Taiwan.

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Two-stream convolutional networks have shown excellent performance in video action recognition in recent years. However, it remains unclear how to model the correlation between the temporal and spatial streams more effectively. First, the spatial stream and temporal stream pay attention to different aspects, which can lead to different recognition results. Second, the variety in the length of optical flow fields tends to have a great impact on the classification results. In this paper, we propose a novel multi-scale spatiotemporal information fusion network to fuse the spatial and temporal features. Specifically, our network takes advantage of multi-scale temporal information to better utilize the motion cues. Considering the complementary relationship between the spatial and temporal features, we take the hierarchical fusion strategies and asynchronous fusion method to fuse the two-stream features. Experimental results on two benchmark datasets (UCF101 and HMDB51) show that the proposed network achieves competitive performance.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Asynchronous, multi-scale spatiotemporal information ,hierarchical fusion strategies
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 04 Feb 2022 03:41
Last Modified: 04 Feb 2022 03:41


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