Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visual-Semantic Graph Attention Networks for Human-Object Interaction
  Detection   In scene understanding, robotics benefit from not only detecting individual
scene instances but also from learning their possible interactions.
Human-Object Interaction (HOI) Detection infers the action predicate on a
<human, predicate, object> triplet. Contextual information has been found
critical in inferring interactions. However, most works only use local features
from single human-object pair for inference. Few works have studied the
disambiguating contribution of subsidiary relations made available via graph
networks. Similarly, few have learned to effectively leverage visual cues along
with the intrinsic semantic regularities contained in HOIs. We contribute a
dual-graph attention network that effectively aggregates contextual visual,
spatial, and semantic information dynamically from primary human-object
relations as well as subsidiary relations through attention mechanisms for
strong disambiguating power. We achieve comparable results on two benchmarks:
V-COCO and HICO-DET. Code is available at
\url{https://github.com/birlrobotics/vs-gats}.

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The categories of this paper are: gr-qc
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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: nlin
28
The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
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The categories of this paper are: econ
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The categories of this paper are: plasm-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: math