Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Domain Adaptation for Semantic Segmentation of NIR Images
  through Generative Latent Search   Segmentation of the pixels corresponding to human skin is an essential first
step in multiple applications ranging from surveillance to heart-rate
estimation from remote-photoplethysmography. However, the existing literature
considers the problem only in the visible-range of the EM-spectrum which limits
their utility in low or no light settings where the criticality of the
application is higher. To alleviate this problem, we consider the problem of
skin segmentation from the Near-infrared images. However, Deep learning based
state-of-the-art segmentation techniques demands large amounts of labelled data
that is unavailable for the current problem. Therefore we cast the skin
segmentation problem as that of target-independent Unsupervised Domain
Adaptation (UDA) where we use the data from the Red-channel of the
visible-range to develop skin segmentation algorithm on NIR images. We propose
a method for target-independent segmentation where the 'nearest-clone' of a
target image in the source domain is searched and used as a proxy in the
segmentation network trained only on the source domain. We prove the existence
of 'nearest-clone' and propose a method to find it through an optimization
algorithm over the latent space of a Deep generative model based on variational
inference. We demonstrate the efficacy of the proposed method for NIR skin
segmentation over the state-of-the-art UDA segmentation methods on the two
newly created skin segmentation datasets in NIR domain despite not having
access to the target NIR data. Additionally, we report state-of-the-art results
for adaption from Synthia to Cityscapes which is a popular setting in
Unsupervised Domain Adaptation for semantic segmentation. The code and datasets
are available at https://github.com/ambekarsameer96/GLSS.

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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ex
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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: math
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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
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The categories of this paper are: supr-con
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The categories of this paper are: atom-ph
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The categories of this paper are: econ