Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gradient-Based Quantification of Epistemic Uncertainty for Deep Object
  Detectors   The vast majority of uncertainty quantification methods for deep object
detectors such as variational inference are based on the network output. Here,
we study gradient-based epistemic uncertainty metrics for deep object detectors
to obtain reliable confidence estimates. We show that they contain predictive
information and that they capture information orthogonal to that of common,
output-based uncertainty estimation methods like Monte-Carlo dropout and deep
ensembles. To this end, we use meta classification and meta regression to
produce confidence estimates using gradient metrics and other baselines for
uncertainty quantification which are in principle applicable to any object
detection architecture. Specifically, we employ false positive detection and
prediction of localization quality to investigate uncertainty content of our
metrics and compute the calibration errors of meta classifiers. Moreover, we
use them as a post-processing filter mechanism to the object detection pipeline
and compare object detection performance. Our results show that gradient-based
uncertainty is itself on par with output-based methods across different
detectors and datasets. More significantly, combined meta classifiers based on
gradient and output-based metrics outperform the standalone models. Based on
this result, we conclude that gradient uncertainty adds orthogonal information
to output-based methods. This suggests that variational inference may be
supplemented by gradient-based uncertainty to obtain improved confidence
measures, contributing to down-stream applications of deep object detectors and
improving their probabilistic reliability.

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The categories of this paper are: eess
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The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
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The categories of this paper are: q-alg
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The categories of this paper are: ao-sci
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The categories of this paper are: atom-ph
2
The categories of this paper are: math
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The categories of this paper are: hep-th
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The categories of this paper are: nlin
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The categories of this paper are: q-bio
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg