Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object
  Detectors   When incorporating deep neural networks into robotic systems, a major
challenge is the lack of uncertainty measures associated with their output
predictions. Methods for uncertainty estimation in the output of deep object
detectors (DNNs) have been proposed in recent works, but have had limited
success due to 1) information loss at the detectors non-maximum suppression
(NMS) stage, and 2) failure to take into account the multitask, many-to-one
nature of anchor-based object detection. To that end, we introduce BayesOD, an
uncertainty estimation approach that reformulates the standard object detector
inference and Non-Maximum suppression components from a Bayesian perspective.
Experiments performed on four common object detection datasets show that
BayesOD provides uncertainty estimates that are better correlated with the
accuracy of detections, manifesting as a significant reduction of
9.77\%-13.13\% on the minimum Gaussian uncertainty error metric and a reduction
of 1.63\%-5.23\% on the minimum Categorical uncertainty error metric. Code will
be released at {\url{https://github.com/asharakeh/bayes-od-rc}}.

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The categories of this paper are: stat
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int