Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Supervised Audio-Visual Co-Segmentation   Segmenting objects in images and separating sound sources in audio are
challenging tasks, in part because traditional approaches require large amounts
of labeled data. In this paper we develop a neural network model for visual
object segmentation and sound source separation that learns from natural videos
through self-supervision. The model is an extension of recently proposed work
that maps image pixels to sounds. Here, we introduce a learning approach to
disentangle concepts in the neural networks, and assign semantic categories to
network feature channels to enable independent image segmentation and sound
source separation after audio-visual training on videos. Our evaluations show
that the disentangled model outperforms several baselines in semantic
segmentation and sound source separation.

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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
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The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
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The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: q-alg
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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: chao-dyn