Boundary Uncertainty in a Single-Stage Temporal Action Localization Network

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 ℓ1 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.