Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Target Detection and Segmentation in Circular-Scan
  Synthetic-Aperture-Sonar Images using Semi-Supervised Convolutional
  Encoder-Decoders   We propose a framework for saliency-based, multi-target detection and
segmentation of circular-scan, synthetic-aperture-sonar (CSAS) imagery. Our
framework relies on a multi-branch, convolutional encoder-decoder network
(MB-CEDN). The encoder portion of the MB-CEDN extracts visual contrast features
from CSAS images. These features are fed into dual decoders that perform
pixel-level segmentation to mask targets. Each decoder provides different
perspectives as to what constitutes a salient target. These opinions are
aggregated and cascaded into a deep-parsing network to refine the segmentation.
  We evaluate our framework using real-world CSAS imagery consisting of five
broad target classes. We compare against existing approaches from the
computer-vision literature. We show that our framework outperforms supervised,
deep-saliency networks designed for natural imagery. It greatly outperforms
unsupervised saliency approaches developed for natural imagery. This
illustrates that natural-image-based models may need to be altered to be
effective for this imaging-sonar modality.

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The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
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The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
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The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
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The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: astro-ph