Computer Science > Computer Vision and Pattern Recognition
[Submitted on 2 Jun 2018 (v1), last revised 20 Jul 2018 (this version, v2)]
Title:Squeeze-and-Excitation on Spatial and Temporal Deep Feature Space for Action Recognition
View PDFAbstract:Spatial and temporal features are two key and complementary information for human action recognition. In order to make full use of the intra-frame spatial characteristics and inter-frame temporal relationships, we propose the Squeeze-and-Excitation Long-term Recurrent Convolutional Networks (SE-LRCN) for human action recognition. The Squeeze and Excitation operations are used to implement the feature recalibration. In SE-LRCN, Squeeze-and-Excitation ResNet-34 (SE-ResNet-34) network is adopted to extract spatial features to enhance the dependencies and importance of feature channels of pixel granularity. We also propose the Squeeze-and-Excitation Long Short-Term Memory (SE-LSTM) network to model the temporal relationship, and to enhance the dependencies and importance of feature channels of frame granularity. We evaluate the proposed model on two challenging benchmarks, HMDB51 and UCF101, and the proposed SE-LRCN achieves the competitive results with the state-of-the-art.
Submission history
From: Zhenxing Zheng [view email][v1] Sat, 2 Jun 2018 13:09:50 UTC (1,276 KB)
[v2] Fri, 20 Jul 2018 02:14:33 UTC (561 KB)
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