Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multiscale IoU: A Metric for Evaluation of Salient Object Detection with
  Fine Structures   General-purpose object-detection algorithms often dismiss the fine structure
of detected objects. This can be traced back to how their proposed regions are
evaluated. Our goal is to renegotiate the trade-off between the generality of
these algorithms and their coarse detections. In this work, we present a new
metric that is a marriage of a popular evaluation metric, namely Intersection
over Union (IoU), and a geometrical concept, called fractal dimension. We
propose Multiscale IoU (MIoU) which allows comparison between the detected and
ground-truth regions at multiple resolution levels. Through several
reproducible examples, we show that MIoU is indeed sensitive to the fine
boundary structures which are completely overlooked by IoU and f1-score. We
further examine the overall reliability of MIoU by comparing its distribution
with that of IoU on synthetic and real-world datasets of objects. We intend
this work to re-initiate exploration of new evaluation methods for
object-detection algorithms.

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The categories of this paper are: q-bio
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The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph
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The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-th
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The categories of this paper are: econ
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: stat
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The categories of this paper are: supr-con