Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Few-Shot Classification in Unseen Domains by Episodic Meta-Learning
  Across Visual Domains   Few-shot classification aims to carry out classification given only few
labeled examples for the categories of interest. Though several approaches have
been proposed, most existing few-shot learning (FSL) models assume that base
and novel classes are drawn from the same data domain. When it comes to
recognizing novel-class data in an unseen domain, this becomes an even more
challenging task of domain generalized few-shot classification. In this paper,
we present a unique learning framework for domain-generalized few-shot
classification, where base classes are from homogeneous multiple source
domains, while novel classes to be recognized are from target domains which are
not seen during training. By advancing meta-learning strategies, our learning
framework exploits data across multiple source domains to capture
domain-invariant features, with FSL ability introduced by metric-learning based
mechanisms across support and query data. We conduct extensive experiments to
verify the effectiveness of our proposed learning framework and show learning
from small yet homogeneous source data is able to perform preferably against
learning from large-scale one. Moreover, we provide insights into choices of
backbone models for domain-generalized few-shot classification.

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The categories of this paper are: atom-ph
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
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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: hep-th
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The categories of this paper are: stat
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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ex