Hierarchical Self-Attention Network for Action Localization in Videos

Rizard Renanda Adhi Pramono, Yie-Tarng Chen, Wen-Hsien Fang; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 61-70

Abstract


This paper presents a novel Hierarchical Self-Attention Network (HISAN) to generate spatial-temporal tubes for action localization in videos. The essence of HISAN is to combine the two-stream convolutional neural network (CNN) with hierarchical bidirectional self-attention mechanism, which comprises of two levels of bidirectional self-attention to efficaciously capture both of the long-term temporal dependency information and spatial context information to render more precise action localization. Also, a sequence rescoring (SR) algorithm is employed to resolve the dilemma of inconsistent detection scores incurred by occlusion or background clutter. Moreover, a new fusion scheme is invoked, which integrates not only the appearance and motion information from the two-stream network, but also the motion saliency to mitigate the effect of camera motion. Simulations reveal that the new approach achieves competitive performance as the state-of-the-art works in terms of action localization and recognition accuracy on the widespread UCF101-24 and J-HMDB datasets.

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[bibtex]
@InProceedings{Pramono_2019_ICCV,
author = {Pramono, Rizard Renanda Adhi and Chen, Yie-Tarng and Fang, Wen-Hsien},
title = {Hierarchical Self-Attention Network for Action Localization in Videos},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}