Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multiple Sound Sources Localization from Coarse to Fine   How to visually localize multiple sound sources in unconstrained videos is a
formidable problem, especially when lack of the pairwise sound-object
annotations. To solve this problem, we develop a two-stage audiovisual learning
framework that disentangles audio and visual representations of different
categories from complex scenes, then performs cross-modal feature alignment in
a coarse-to-fine manner. Our model achieves state-of-the-art results on public
dataset of localization, as well as considerable performance on multi-source
sound localization in complex scenes. We then employ the localization results
for sound separation and obtain comparable performance to existing methods.
These outcomes demonstrate our model's ability in effectively aligning sounds
with specific visual sources. Code is available at
https://github.com/shvdiwnkozbw/Multi-Source-Sound-Localization

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The categories of this paper are: alg-geom
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The categories of this paper are: stat
10
The categories of this paper are: q-bio
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-ex
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-alg
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ph
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The categories of this paper are: nlin
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The categories of this paper are: chem-ph
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The categories of this paper are: bayes-an