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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

StNet: Local and Global Spatial-Temporal Modeling for Action Recognition

February 1, 2023

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Authors

Dongliang He

Baidu, Inc.


Zhichao Zhou

Baidu, Inc.


Chuang Gan

Massachusetts Institute of Technology


Fu Li

Baidu, Inc.


Xiao Liu

Baidu, Inc.


Yandong Li

University of Central Florida


Limin Wang

Nanjing University


Shilei Wen

Baidu Research


DOI:

10.1609/aaai.v33i01.33018401


Abstract:

Despite the success of deep learning for static image understanding, it remains unclear what are the most effective network architectures for spatial-temporal modeling in videos. In this paper, in contrast to the existing CNN+RNN or pure 3D convolution based approaches, we explore a novel spatialtemporal network (StNet) architecture for both local and global modeling in videos. Particularly, StNet stacks N successive video frames into a super-image which has 3N channels and applies 2D convolution on super-images to capture local spatial-temporal relationship. To model global spatialtemporal structure, we apply temporal convolution on the local spatial-temporal feature maps. Specifically, a novel temporal Xception block is proposed in StNet, which employs a separate channel-wise and temporal-wise convolution over the feature sequence of a video. Extensive experiments on the Kinetics dataset demonstrate that our framework outperforms several state-of-the-art approaches in action recognition and can strike a satisfying trade-off between recognition accuracy and model complexity. We further demonstrate the generalization performance of the leaned video representations on the UCF101 dataset.

Topics: AAAI

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HOW TO CITE:

Dongliang He||Zhichao Zhou||Chuang Gan||Fu Li||Xiao Liu||Yandong Li||Limin Wang||Shilei Wen StNet: Local and Global Spatial-Temporal Modeling for Action Recognition Proceedings of the AAAI Conference on Artificial Intelligence (2019) 8401-8408.

Dongliang He||Zhichao Zhou||Chuang Gan||Fu Li||Xiao Liu||Yandong Li||Limin Wang||Shilei Wen StNet: Local and Global Spatial-Temporal Modeling for Action Recognition AAAI 2019, 8401-8408.

Dongliang He||Zhichao Zhou||Chuang Gan||Fu Li||Xiao Liu||Yandong Li||Limin Wang||Shilei Wen (2019). StNet: Local and Global Spatial-Temporal Modeling for Action Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 8401-8408.

Dongliang He||Zhichao Zhou||Chuang Gan||Fu Li||Xiao Liu||Yandong Li||Limin Wang||Shilei Wen. StNet: Local and Global Spatial-Temporal Modeling for Action Recognition. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.8401-8408.

Dongliang He||Zhichao Zhou||Chuang Gan||Fu Li||Xiao Liu||Yandong Li||Limin Wang||Shilei Wen. 2019. StNet: Local and Global Spatial-Temporal Modeling for Action Recognition. "Proceedings of the AAAI Conference on Artificial Intelligence". 8401-8408.

Dongliang He||Zhichao Zhou||Chuang Gan||Fu Li||Xiao Liu||Yandong Li||Limin Wang||Shilei Wen. (2019) "StNet: Local and Global Spatial-Temporal Modeling for Action Recognition", Proceedings of the AAAI Conference on Artificial Intelligence, p.8401-8408

Dongliang He||Zhichao Zhou||Chuang Gan||Fu Li||Xiao Liu||Yandong Li||Limin Wang||Shilei Wen, "StNet: Local and Global Spatial-Temporal Modeling for Action Recognition", AAAI, p.8401-8408, 2019.

Dongliang He||Zhichao Zhou||Chuang Gan||Fu Li||Xiao Liu||Yandong Li||Limin Wang||Shilei Wen. "StNet: Local and Global Spatial-Temporal Modeling for Action Recognition". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.8401-8408.

Dongliang He||Zhichao Zhou||Chuang Gan||Fu Li||Xiao Liu||Yandong Li||Limin Wang||Shilei Wen. "StNet: Local and Global Spatial-Temporal Modeling for Action Recognition". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 8401-8408.

Dongliang He||Zhichao Zhou||Chuang Gan||Fu Li||Xiao Liu||Yandong Li||Limin Wang||Shilei Wen. StNet: Local and Global Spatial-Temporal Modeling for Action Recognition. AAAI[Internet]. 2019[cited 2023]; 8401-8408.


ISSN: 2374-3468


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