Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Make an Omelette with Breaking Eggs: Zero-Shot Learning for Novel
  Attribute Synthesis   Most of the existing algorithms for zero-shot classification problems
typically rely on the attribute-based semantic relations among categories to
realize the classification of novel categories without observing any of their
instances. However, training the zero-shot classification models still requires
attribute labeling for each class (or even instance) in the training dataset,
which is also expensive. To this end, in this paper, we bring up a new problem
scenario: "Are we able to derive zero-shot learning for novel attribute
detectors/classifiers and use them to automatically annotate the dataset for
labeling efficiency?" Basically, given only a small set of detectors that are
learned to recognize some manually annotated attributes (i.e., the seen
attributes), we aim to synthesize the detectors of novel attributes in a
zero-shot learning manner. Our proposed method, Zero Shot Learning for
Attributes (ZSLA), which is the first of its kind to the best of our knowledge,
tackles this new research problem by applying the set operations to first
decompose the seen attributes into their basic attributes and then recombine
these basic attributes into the novel ones. Extensive experiments are conducted
to verify the capacity of our synthesized detectors for accurately capturing
the semantics of the novel attributes and show their superior performance in
terms of detection and localization compared to other baseline approaches.
Moreover, with using only 32 seen attributes on the Caltech-UCSD Birds-200-2011
dataset, our proposed method is able to synthesize other 207 novel attributes,
while various generalized zero-shot classification algorithms trained upon the
dataset re-annotated by our synthesized attribute detectors are able to provide
comparable performance with those trained with the manual ground-truth
annotations.

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The categories of this paper are: math
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The categories of this paper are: physics
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The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: econ
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The categories of this paper are: hep-th
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The categories of this paper are: nlin