5,421 research outputs found
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
Pre-training strategies and datasets for facial representation learning
What is the best way to learn a universal face representation? Recent work on
Deep Learning in the area of face analysis has focused on supervised learning
for specific tasks of interest (e.g. face recognition, facial landmark
localization etc.) but has overlooked the overarching question of how to find a
facial representation that can be readily adapted to several facial analysis
tasks and datasets. To this end, we make the following 4 contributions: (a) we
introduce, for the first time, a comprehensive evaluation benchmark for facial
representation learning consisting of 5 important face analysis tasks. (b) We
systematically investigate two ways of large-scale representation learning
applied to faces: supervised and unsupervised pre-training. Importantly, we
focus our evaluations on the case of few-shot facial learning. (c) We
investigate important properties of the training datasets including their size
and quality (labelled, unlabelled or even uncurated). (d) To draw our
conclusions, we conducted a very large number of experiments. Our main two
findings are: (1) Unsupervised pre-training on completely in-the-wild,
uncurated data provides consistent and, in some cases, significant accuracy
improvements for all facial tasks considered. (2) Many existing facial video
datasets seem to have a large amount of redundancy. We will release code, and
pre-trained models to facilitate future research.Comment: Accepted at ECCV 202
Affective Image Content Analysis: Two Decades Review and New Perspectives
Images can convey rich semantics and induce various emotions in viewers.
Recently, with the rapid advancement of emotional intelligence and the
explosive growth of visual data, extensive research efforts have been dedicated
to affective image content analysis (AICA). In this survey, we will
comprehensively review the development of AICA in the recent two decades,
especially focusing on the state-of-the-art methods with respect to three main
challenges -- the affective gap, perception subjectivity, and label noise and
absence. We begin with an introduction to the key emotion representation models
that have been widely employed in AICA and description of available datasets
for performing evaluation with quantitative comparison of label noise and
dataset bias. We then summarize and compare the representative approaches on
(1) emotion feature extraction, including both handcrafted and deep features,
(2) learning methods on dominant emotion recognition, personalized emotion
prediction, emotion distribution learning, and learning from noisy data or few
labels, and (3) AICA based applications. Finally, we discuss some challenges
and promising research directions in the future, such as image content and
context understanding, group emotion clustering, and viewer-image interaction.Comment: Accepted by IEEE TPAM
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