LSTA: Long Short-Term Attention for Egocentric Action Recognition

Swathikiran Sudhakaran, Sergio Escalera, Oswald Lanz; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 9954-9963

Abstract


Egocentric activity recognition is one of the most challenging tasks in video analysis. It requires a fine-grained discrimination of small objects and their manipulation. While some methods base on strong supervision and attention mechanisms, they are either annotation consuming or do not take spatio-temporal patterns into account. In this paper we propose LSTA as a mechanism to focus on features from spatial relevant parts while attention is being tracked smoothly across the video sequence. We demonstrate the effectiveness of LSTA on egocentric activity recognition with an end-to-end trainable two-stream architecture, achieving state-of-the-art performance on four standard benchmarks.

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[bibtex]
@InProceedings{Sudhakaran_2019_CVPR,
author = {Sudhakaran, Swathikiran and Escalera, Sergio and Lanz, Oswald},
title = {LSTA: Long Short-Term Attention for Egocentric Action Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}