Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ACP++: Action Co-occurrence Priors for Human-Object Interaction
  Detection   A common problem in the task of human-object interaction (HOI) detection is
that numerous HOI classes have only a small number of labeled examples,
resulting in training sets with a long-tailed distribution. The lack of
positive labels can lead to low classification accuracy for these classes.
Towards addressing this issue, we observe that there exist natural correlations
and anti-correlations among human-object interactions. In this paper, we model
the correlations as action co-occurrence matrices and present techniques to
learn these priors and leverage them for more effective training, especially on
rare classes. The efficacy of our approach is demonstrated experimentally,
where the performance of our approach consistently improves over the
state-of-the-art methods on both of the two leading HOI detection benchmark
datasets, HICO-Det and V-COCO.

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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
2
The categories of this paper are: math
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The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
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The categories of this paper are: stat
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The categories of this paper are: alg-geom
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The categories of this paper are: econ
10
The categories of this paper are: q-bio