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Local-aware Spatio-temporal Attention Network with Multi-stage Feature Fusion for Human Action Recognition by Yaqing Hou; Hua Yu; Dongsheng Zhou; Pengfei Wang; Hongwei Ge; Jianxin Zhang; Qiang Zhang is a Computer Science article available to read on EtoBox.

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## Abstract In the study of human action recognition, two-stream networks have made excellent progress recently. However, there remain challenges in distinguishing similar human actions in videos. This paper proposes a novel local-aware spatio-temporal attention network with multi-stage feature fusion based on compact bilinear pooling for human action recognition. To elaborate, taking two-stream networks as our essential backbones, the spatial network first employs multiple spatial transformer networks in a parallel manner to locate the discriminative regions related to human actions. Then, we perform feature fusion between the local and global features to enhance the human action representation. Furthermore, the output of the spatial network and the temporal information are fused at a particular layer to learn the pixel-wise correspondences. After that, we bring together three outputs to generate the global descriptors of human actions. To verify the efficacy of the proposed approach, comparison experiments are conducted with the traditional hand-engineered IDT algorithms, the classical machine learning methods (i.e., SVM) and the state-of-the-art deep learning methods (i.e., spat

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Author

Yaqing Hou; Hua Yu; Dongsheng Zhou; Pengfei Wang; Hongwei Ge; Jianxin Zhang; Qiang Zhang

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)