Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Image Decomposition with Phase-Correlation Networks   The ability to decompose scenes into their object components is a desired
property for autonomous agents, allowing them to reason and act in their
surroundings. Recently, different methods have been proposed to learn
object-centric representations from data in an unsupervised manner. These
methods often rely on latent representations learned by deep neural networks,
hence requiring high computational costs and large amounts of curated data.
Such models are also difficult to interpret. To address these challenges, we
propose the Phase-Correlation Decomposition Network (PCDNet), a novel model
that decomposes a scene into its object components, which are represented as
transformed versions of a set of learned object prototypes. The core building
block in PCDNet is the Phase-Correlation Cell (PC Cell), which exploits the
frequency-domain representation of the images in order to estimate the
transformation between an object prototype and its transformed version in the
image. In our experiments, we show how PCDNet outperforms state-of-the-art
methods for unsupervised object discovery and segmentation on simple benchmark
datasets and on more challenging data, while using a small number of learnable
parameters and being fully interpretable.

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The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
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The categories of this paper are: nlin
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The categories of this paper are: adap-org
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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph