Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Audio-Visual Subspace Alignment for High-Stakes Deception
  Detection   Automated systems that detect deception in high-stakes situations can enhance
societal well-being across medical, social work, and legal domains. Existing
models for detecting high-stakes deception in videos have been supervised, but
labeled datasets to train models can rarely be collected for most real-world
applications. To address this problem, we propose the first multimodal
unsupervised transfer learning approach that detects real-world, high-stakes
deception in videos without using high-stakes labels. Our subspace-alignment
(SA) approach adapts audio-visual representations of deception in
lab-controlled low-stakes scenarios to detect deception in real-world,
high-stakes situations. Our best unsupervised SA models outperform models
without SA, outperform human ability, and perform comparably to a number of
existing supervised models. Our research demonstrates the potential for
introducing subspace-based transfer learning to model high-stakes deception and
other social behaviors in real-world contexts with a scarcity of labeled
behavioral data.

21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom