Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Discriminative Sounding Objects Localization via Self-supervised
  Audiovisual Matching   Discriminatively localizing sounding objects in cocktail-party, i.e., mixed
sound scenes, is commonplace for humans, but still challenging for machines. In
this paper, we propose a two-stage learning framework to perform
self-supervised class-aware sounding object localization. First, we propose to
learn robust object representations by aggregating the candidate sound
localization results in the single source scenes. Then, class-aware object
localization maps are generated in the cocktail-party scenarios by referring
the pre-learned object knowledge, and the sounding objects are accordingly
selected by matching audio and visual object category distributions, where the
audiovisual consistency is viewed as the self-supervised signal. Experimental
results in both realistic and synthesized cocktail-party videos demonstrate
that our model is superior in filtering out silent objects and pointing out the
location of sounding objects of different classes. Code is available at
https://github.com/DTaoo/Discriminative-Sounding-Objects-Localization.

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The categories of this paper are: funct-an
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The categories of this paper are: math
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The categories of this paper are: patt-sol
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The categories of this paper are: q-fin
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The categories of this paper are: stat
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The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
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The categories of this paper are: ao-sci
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The categories of this paper are: supr-con
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The categories of this paper are: hep-th
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The categories of this paper are: physics
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The categories of this paper are: alg-geom