Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards a General Deep Feature Extractor for Facial Expression
  Recognition   The human face conveys a significant amount of information. Through facial
expressions, the face is able to communicate numerous sentiments without the
need for verbalisation. Visual emotion recognition has been extensively
studied. Recently several end-to-end trained deep neural networks have been
proposed for this task. However, such models often lack generalisation ability
across datasets. In this paper, we propose the Deep Facial Expression Vector
ExtractoR (DeepFEVER), a new deep learning-based approach that learns a visual
feature extractor general enough to be applied to any other facial emotion
recognition task or dataset. DeepFEVER outperforms state-of-the-art results on
the AffectNet and Google Facial Expression Comparison datasets. DeepFEVER's
extracted features also generalise extremely well to other datasets -- even
those unseen during training -- namely, the Real-World Affective Faces (RAF)
dataset.

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The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: stat
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: q-alg