Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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 $\ell_1$ 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.

13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph