[Seminar] On the Challenges of Modelling Complex Human Behaviors from Visual Information
Friday, February 26, 2021
11:00 am - 12:00 pm
Location:
Online
via
MS
Teams
Speaker:
Dr.
Hugo
Escalante
Abstract:
Looking
at
People
(LaP)
is
the
field
of
computer
vision
dealing
with
the
analysis
of
human
behavior
from
visual
information.
Great
improvements
have
been
reported
in
this
field
for
the
so-called
“obviously
visual”
behaviors
(e.g.,
gesture
recognition,
pose
estimation,
etc.).
However,
it
is
only
recently
that
the
community
is
targeting
more
complex
human
behaviours
that
are
not
visually
evident
and
therefore
require
additional
information
and
specialized
mechanisms
for
their
analysis.
In
this
talk
I
will
describe
past
and
ongoing
work
on
the
automated
analysis
of
such
subconscious
human
behaviors
by
using
multimodal
information.
Focusing
on
the
inherent
difficulties
of
these
tasks,
resources,
and
open
problems.
About
the
Speaker:
Dr.
Hugo
Escalante
is
a
senior
research
scientist
at
INAOE,
Mexico
and
secretary
and
member
of
the
board
of
directors
of
Cha
Learn
USA,
Chair
officer
of
the
IAPR
Technical
Committee
12.
He
is
a
member
of
the
Mexican
Academy
of
Sciences
(AMC),
the
Mexican
Academy
of
Computing
(AMEXCOMP)
and
Mexican
System
of
Researchers
Level
II
(SNI).
Since
2017,
he
is
editor
of
the
Springer
Series
on
Challenges
in
Machine
Learning.
He
has
been
involved
in
the
organization
of
several
challenges
in
machine
learning
and
computer
vision
collocated
with
top
venues,
see
http://chalearnlap.cvc.uab.es/.
He
has
served
as
co-editor
of
special
issues
in
IJCV,
IEEE
TPAMI,
and
IEEE
Transactions
on
Affective
Computing.
He
has
served
as
competition
chair
of
NeurIPS2020,
FG2020
and
ICPR2020,
NeurIPS2019,
PAKDD2019-2018,
IJCNN2019.
His
research
interests
are
on
machine
learning,
challenge
organization,
and
its
applications
on
language
and
vision.
- Location
- Online via MS Teams