Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self Supervision to Distillation for Long-Tailed Visual Recognition   Deep learning has achieved remarkable progress for visual recognition on
large-scale balanced datasets but still performs poorly on real-world
long-tailed data. Previous methods often adopt class re-balanced training
strategies to effectively alleviate the imbalance issue, but might be a risk of
over-fitting tail classes. The recent decoupling method overcomes over-fitting
issues by using a multi-stage training scheme, yet, it is still incapable of
capturing tail class information in the feature learning stage. In this paper,
we show that soft label can serve as a powerful solution to incorporate label
correlation into a multi-stage training scheme for long-tailed recognition. The
intrinsic relation between classes embodied by soft labels turns out to be
helpful for long-tailed recognition by transferring knowledge from head to tail
classes.
  Specifically, we propose a conceptually simple yet particularly effective
multi-stage training scheme, termed as Self Supervised to Distillation (SSD).
This scheme is composed of two parts. First, we introduce a self-distillation
framework for long-tailed recognition, which can mine the label relation
automatically. Second, we present a new distillation label generation module
guided by self-supervision. The distilled labels integrate information from
both label and data domains that can model long-tailed distribution
effectively. We conduct extensive experiments and our method achieves the
state-of-the-art results on three long-tailed recognition benchmarks:
ImageNet-LT, CIFAR100-LT and iNaturalist 2018. Our SSD outperforms the strong
LWS baseline by from $2.7\%$ to $4.5\%$ on various datasets. The code is
available at https://github.com/MCG-NJU/SSD-LT.

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The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
26
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: stat
4
The categories of this paper are: cond-mat
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The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
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The categories of this paper are: cmp-lg
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The categories of this paper are: adap-org
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: q-alg