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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