| G. Pikramenos, E. Mathe, E. Vali, I. Vernikos, A. Papadakis, E. Spyrou, Ph. Mylonas |
| An Adversarial Semi-Supervised Approach to Action Recognition from Pose Information |
| Neural Computing and Applications, Springer, June 2020 |
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ABSTRACT
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| The collection of video data for action recognition is very susceptible to measurement bias; the equipment used, camera angle and environmental conditions are all factors that majorly affect the distribution of the collected dataset. Inevitably, training a classifier that can successfully generalize to new data becomes a very hard problem, since it is impossible to gather general enough training sets. Recent approaches in the literature attempt to solve this problem by augmenting a given training set, with synthetic data, so as to better represent the global distribution of the covariates. However, these approaches are limited because they essentially involve hand-crafted data synthesizers, which are typically hard to implement and problem specific. In this work, we propose a different approach to tackling the above issues, which relies on the combination of two techniques: pose extraction, and domain adaptation as a means to improve the generalization capabilities of classifiers. We show that adapted skeletal representations can be retrieved automatically in a semi-supervised setting and these help to generalize classifiers to new forms of measurement bias. We empirically validate our approach for generalizing across different camera angles.
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| 23 June , 2020 |
| G. Pikramenos, E. Mathe, E. Vali, I. Vernikos, A. Papadakis, E. Spyrou, Ph. Mylonas, "An Adversarial Semi-Supervised Approach to Action Recognition from Pose Information", Neural Computing and Applications, Springer, June 2020 |
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