Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: False Detection (Positives and Negatives) in Object Detection   Object detection is a very important function of visual perception systems.
Since the early days of classical object detection based on HOG to modern deep
learning based detectors, object detection has improved in accuracy. Two stage
detectors usually have higher accuracy than single stage ones. Both types of
detectors use some form of quantization of the search space of rectangular
regions of image. There are far more of the quantized elements than true
objects. The way these bounding boxes are filtered out possibly results in the
false positive and false negatives. This empirical experimental study explores
ways of reducing false positives and negatives with labelled data.. In the
process also discovered insufficient labelling in Openimage 2019 Object
Detection dataset.

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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: alg-geom
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The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-th
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The categories of this paper are: adap-org