Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning   In zero-shot learning (ZSL), a classifier is trained to recognize visual
classes without any image samples. Instead, it is given semantic information
about the class, like a textual description or a set of attributes. Learning
from attributes could benefit from explicitly modeling structure of the
attribute space. Unfortunately, learning of general structure from empirical
samples is hard with typical dataset sizes.
  Here we describe LAGO, a probabilistic model designed to capture natural soft
and-or relations across groups of attributes. We show how this model can be
learned end-to-end with a deep attribute-detection model. The soft group
structure can be learned from data jointly as part of the model, and can also
readily incorporate prior knowledge about groups if available. The soft and-or
structure succeeds to capture meaningful and predictive structures, improving
the accuracy of zero-shot learning on two of three benchmarks.
  Finally, LAGO reveals a unified formulation over two ZSL approaches: DAP
(Lampert et al., 2009) and ESZSL (Romera-Paredes & Torr, 2015). Interestingly,
taking only one singleton group for each attribute, introduces a new
soft-relaxation of DAP, that outperforms DAP by ~40.

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The categories of this paper are: eess
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The categories of this paper are: math-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ph
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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: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: math
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The categories of this paper are: supr-con
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The categories of this paper are: astro-ph
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The categories of this paper are: nlin