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LAURIE BAYET: My name is Laurie Bayet.
I'm a postdoc at the University of Rochester and Boston
Children's Hospital, and I'm working
on developmental cognitive neuroscience.
ALON BARAM: My name is Alon, and I am studying currently
at Oxford.
I'm doing my PhD.
I'm there with Professor Tim Behrens,
and I'm currently working on computational cognitive
neuroscience.
LAURIE BAYET: Alon and I are trying
to use paper by Tomaso Poggio and Potters on a specific way
to achieve invariant recognition in computer
vision or other algorithm.
So we're basically trying to implement this in a simpler
case and then moving on to our face recognition
under rotations.
ALON BARAM: The idea is that most
of the variance in computer vision,
when an algorithm tries to discover what is in the image,
is held in very few manipulation.
Like translation, which is a shifting image across a field
or rotations or scaling.
So Poggio has a cool idea of how to create this signature
that Laurie just told about, which
is invariant to these things and might reduce the sample
complexity.
So how many examples you need to learn.
LAURIE BAYET: For the simple case,
we just used an existing data set of digits.
For the face data set, we tried to find a suitable data set
online, but we ended up just taking
videos of people using materials provided by the summer school.
So taking videos of people rotating their heads like this
slowly.
ALON BARAM: Yeah, it was fun.
LAURIE BAYET: Moving around a little bit.
ALON BARAM: We have now a complete data
set of the heads of people from different angles.
LAURIE BAYET: We wanted to provide the algorithm
with a hopefully limited number of raw frames
from people rotating their heads like this.
As a template, so to speak, and act then as like a kernel
so to speak, to be able then to recognize
unseen people under various angles
so that whenever a person is showing
this profile or this profile, you
would still be able to recognize it
with the same level of accuracy as
if they were in front of them, presenting the frontal face.
ALON BARAM: The purpose of doing this project would
be, in the long run or what this iTheory as Tommy Poggio calls
it will be in the long run would be
to reduce the number of examples that an algorithm, for example,
deep neural nets, the number of examples
they need to see in order to learn their weights in order
to learn how to classify images or retrieve images.
LAURIE BAYET: We haven't started the face part.
We only started the digits part, which worked.
So we're--
ALON BARAM: It's working basically.
We hope it will also work in the endlessly more complex domain
of faces.
LAURIE BAYET: Now you know.
ALON BARAM: But we're hoping.
LAURIE BAYET: We're reasonably optimistic.
I don't know.
We'll see.
ALON BARAM: Fingers crossed.
LAURIE BAYET: We've approached the project
from pretty much very different angles
but still ended up having common interests, which
I guess is kind of hallmark of this summer school too.
Alon has this other, very interested in the engineering
problems, so to speak.
So how can we achieve this with machines?
And I approached the project from
a developmental perspective.
So given that the current algorithms
manage to do invariant face recognition based
on a fairly large number of exemplars,
how come infants can achieve this in a few months
based on a lot of experience, but not that much--
mostly looking at their parents, caregivers,
and a few other exemplars, but not
like 3,000 people from all possible angles.
So this is why I was very interested in this theory
and trying to implement this manually
has been pretty cool so far.
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