Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Selective Zero-Shot Classification with Augmented Attributes   In this paper, we introduce a selective zero-shot classification problem: how
can the classifier avoid making dubious predictions? Existing attribute-based
zero-shot classification methods are shown to work poorly in the selective
classification scenario. We argue the under-complete human defined attribute
vocabulary accounts for the poor performance. We propose a selective zero-shot
classifier based on both the human defined and the automatically discovered
residual attributes. The proposed classifier is constructed by firstly learning
the defined and the residual attributes jointly. Then the predictions are
conducted within the subspace of the defined attributes. Finally, the
prediction confidence is measured by both the defined and the residual
attributes. Experiments conducted on several benchmarks demonstrate that our
classifier produces a superior performance to other methods under the
risk-coverage trade-off metric.

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The categories of this paper are: q-alg
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The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
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The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
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The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
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The categories of this paper are: eess
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The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg
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The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an