Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MHFC: Multi-Head Feature Collaboration for Few-Shot Learning   Few-shot learning (FSL) aims to address the data-scarce problem. A standard
FSL framework is composed of two components: (1) Pre-train. Employ the base
data to generate a CNN-based feature extraction model (FEM). (2) Meta-test.
Apply the trained FEM to acquire the novel data's features and recognize them.
FSL relies heavily on the design of the FEM. However, various FEMs have
distinct emphases. For example, several may focus more attention on the contour
information, whereas others may lay particular emphasis on the texture
information. The single-head feature is only a one-sided representation of the
sample. Besides the negative influence of cross-domain (e.g., the trained FEM
can not adapt to the novel class flawlessly), the distribution of novel data
may have a certain degree of deviation compared with the ground truth
distribution, which is dubbed as distribution-shift-problem (DSP). To address
the DSP, we propose Multi-Head Feature Collaboration (MHFC) algorithm, which
attempts to project the multi-head features (e.g., multiple features extracted
from a variety of FEMs) to a unified space and fuse them to capture more
discriminative information. Typically, first, we introduce a subspace learning
method to transform the multi-head features to aligned low-dimensional
representations. It corrects the DSP via learning the feature with more
powerful discrimination and overcomes the problem of inconsistent measurement
scales from different head features. Then, we design an attention block to
update combination weights for each head feature automatically. It
comprehensively considers the contribution of various perspectives and further
improves the discrimination of features. We evaluate the proposed method on
five benchmark datasets (including cross-domain experiments) and achieve
significant improvements of 2.1%-7.8% compared with state-of-the-arts.

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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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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: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: funct-an
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The categories of this paper are: acc-phys
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ph
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The categories of this paper are: q-fin