Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial
  Representation   Over the past few years, best SSL methods, gradually moved from the pre-text
task learning to the Contrastive learning. But contrastive methods have some
drawbacks which could not be solved completely, such as performing poor on
fine-grained visual tasks compare to supervised learning methods. In this
study, at first, the impact of ImageNet pre-training on fine-grained Facial
Expression Recognition (FER) was tested. It could be seen from the results that
training from scratch is better than ImageNet fine-tuning at stronger
augmentation levels. After that, a framework was proposed for standard
Supervised Learning (SL), called Hybrid Multi-Task Learning (HMTL) which merged
Self-Supervised as auxiliary task to the SL training setting. Leveraging
Self-Supervised Learning (SSL) can gain additional information from input data
than labels which can help the main fine-grained SL task. It is been
investigated how this method could be used for FER by designing two customized
version of common pre-text techniques, Jigsaw puzzling and in-painting. The
state-of-the-art was reached on AffectNet via two types of HMTL, without
utilizing pre-training on additional datasets. Moreover, we showed the
difference between SS pre-training and HMTL to demonstrate superiority of
proposed method. Furthermore, the impact of proposed method was shown on two
other fine-grained facial tasks, Head Poses estimation and Gender Recognition,
which concluded to reduce in error rate by 11% and 1% respectively.

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The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
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The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
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The categories of this paper are: ao-sci
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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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: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: supr-con