Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PersEmoN: A Deep Network for Joint Analysis of Apparent Personality,
  Emotion and Their Relationship   Apparent personality and emotion analysis are both central to affective
computing. Existing works solve them individually. In this paper we investigate
if such high-level affect traits and their relationship can be jointly learned
from face images in the wild. To this end, we introduce PersEmoN, an end-to-end
trainable and deep Siamese-like network. It consists of two convolutional
network branches, one for emotion and the other for apparent personality. Both
networks share their bottom feature extraction module and are optimized within
a multi-task learning framework. Emotion and personality networks are dedicated
to their own annotated dataset. Furthermore, an adversarial-like loss function
is employed to promote representation coherence among heterogeneous dataset
sources. Based on this, we also explore the emotion-to-apparent-personality
relationship. Extensive experiments demonstrate the effectiveness of PersEmoN.

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The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
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The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
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The categories of this paper are: alg-geom