Boundary Uncertainty in a Single-Stage Temporal Action Localization Network

• URL: http://arxiv.org/abs/2008.11170v1
• Date: Tue, 25 Aug 2020 17:04:39 GMT
• Title: Boundary Uncertainty in a Single-Stage Temporal Action Localization Network
• Authors: Ting-Ting Xie, Christos Tzelepis, Ioannis Patras
• Abstract summary: We show that with both uncertainty modeling approaches improve the detection performance by more than $1.5%$ in mAP@tIoU=0.5. The proposed simple one-stage network performs closely to more complex one and two stage networks.
• Score: 12.364819165688628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we address the problem of temporal action localization with a single stage neural network. In the proposed architecture we model the boundary predictions as uni-variate Gaussian distributions in order to model their uncertainties, which is the first in this area to the best of our knowledge. We use two uncertainty-aware boundary regression losses: first, the Kullback-Leibler divergence between the ground truth location of the boundary and the Gaussian modeling the prediction of the boundary and second, the expectation of the $\ell_1$ loss under the same Gaussian. We show that with both uncertainty modeling approaches improve the detection performance by more than $1.5\%$ in mAP@tIoU=0.5 and that the proposed simple one-stage network performs closely to more complex one and two stage networks.

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