Related papers: Extending Temporal Data Augmentation for Video Action Recognition

Extending Temporal Data Augmentation for Video Action Recognition

URL: http://arxiv.org/abs/2211.04888v1
Date: Wed, 9 Nov 2022 13:49:38 GMT
Title: Extending Temporal Data Augmentation for Video Action Recognition
Authors: Artjoms Gorpincenko, Michal Mackiewicz
Abstract summary: We propose novel techniques to strengthen the relationship between the spatial and temporal domains. The video action recognition results of our techniques outperform their respective variants in Top-1 and Top-5 settings on the UCF-101 and the HMDB-51 datasets.
Score: 1.3807859854345832
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Pixel space augmentation has grown in popularity in many Deep Learning areas, due to its effectiveness, simplicity, and low computational cost. Data augmentation for videos, however, still remains an under-explored research topic, as most works have been treating inputs as stacks of static images rather than temporally linked series of data. Recently, it has been shown that involving the time dimension when designing augmentations can be superior to its spatial-only variants for video action recognition. In this paper, we propose several novel enhancements to these techniques to strengthen the relationship between the spatial and temporal domains and achieve a deeper level of perturbations. The video action recognition results of our techniques outperform their respective variants in Top-1 and Top-5 settings on the UCF-101 and the HMDB-51 datasets.

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