Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Are standard Object Segmentation models sufficient for Learning
  Affordance Segmentation?   Affordances are the possibilities of actions the environment offers to the
individual. Ordinary objects (hammer, knife) usually have many affordances
(grasping, pounding, cutting), and detecting these allow artificial agents to
understand what are their possibilities in the environment, with obvious
application in Robotics. Proposed benchmarks and state-of-the-art prediction
models for supervised affordance segmentation are usually modifications of
popular object segmentation models such as Mask R-CNN. We observe that
theoretically, these popular object segmentation methods should be sufficient
for detecting affordances masks. So we ask the question: is it necessary to
tailor new architectures to the problem of learning affordances? We show that
applying the out-of-the-box Mask R-CNN to the problem of affordances
segmentation outperforms the current state-of-the-art. We conclude that the
problem of supervised affordance segmentation is included in the problem of
object segmentation and argue that better benchmarks for affordance learning
should include action capacities.

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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
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The categories of this paper are: econ
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-th
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The categories of this paper are: nlin
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The categories of this paper are: nucl-th