Annotator-dependent uncertainty-aware estimation of gait relative attributes

Article


Shehata, A., Makihara, Y., Muramatsu, D., Ahad, M. and Yasushi, Y. 2023. Annotator-dependent uncertainty-aware estimation of gait relative attributes. Pattern Recognition. 136 (Art. 109197). https://doi.org/10.1016/j.patcog.2022.109197
AuthorsShehata, A., Makihara, Y., Muramatsu, D., Ahad, M. and Yasushi, Y.
Abstract

In this paper, we describe an uncertainty-aware estimation framework for gait relative attributes. We specifically design a two-stream network model that takes a pair of gait videos as input. It then outputs a corresponding pair of Gaussian distributions of gait absolute attribute scores and annotator-dependent gait relative attribute label distributions. Moreover, we propose a differentiable annotator-independent uncertainty layer to estimate the gait relative attribute score distribution from the absolute distributions then map it to a relative attribute label distribution using the computation of cumulative distribution functions. Furthermore, we propose another annotator-dependent uncertainty layer to estimate the uncertainty on the gait relative attribute labels in terms of a set of trainable transition matrices. Finally, we design a joint loss function on the relative attribute label distribution to learn the model parameters. Experiments on two gait relative attribute datasets demonstrated the effectiveness of the proposed method against baselines in quantitative and qualitative evaluations.

JournalPattern Recognition
Journal citation136 (Art. 109197)
ISSN0031-3203
Year2023
PublisherElsevier
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Anyone
Digital Object Identifier (DOI)https://doi.org/10.1016/j.patcog.2022.109197
Publication dates
Online22 Nov 2022
PrintApr 2023
Publication process dates
Accepted20 Nov 2022
Deposited26 Jul 2023
FunderJapan Society for the Promotion of Science (JSPS)
Copyright holder© 2022, The Author(s)
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https://repository.uel.ac.uk/item/8w4w1

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