Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Leveraging Multi-view Image Sets for Unsupervised Intrinsic Image
  Decomposition and Highlight Separation   We present an unsupervised approach for factorizing object appearance into
highlight, shading, and albedo layers, trained by multi-view real images. To do
so, we construct a multi-view dataset by collecting numerous customer product
photos online, which exhibit large illumination variations that make them
suitable for training of reflectance separation and can facilitate object-level
decomposition. The main contribution of our approach is a proposed image
representation based on local color distributions that allows training to be
insensitive to the local misalignments of multi-view images. In addition, we
present a new guidance cue for unsupervised training that exploits synergy
between highlight separation and intrinsic image decomposition. Over a broad
range of objects, our technique is shown to yield state-of-the-art results for
both of these tasks.

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The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
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The categories of this paper are: stat
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th