[Lisa_seminaires] [Tea Talk] Adrià Recasens (MIT) May 25 2018 1:30 PM AA3195

Michael Noukhovitch mnoukhov at gmail.com
Mer 23 Mai 16:21:25 EDT 2018


For our second talk this Friday, we have *Adrià Recasens * from * MIT
*on *Friday
May 25 2018* at *1:30 PM* in room *AA3195*

This talk will be streamed as usual here:
https://bluejeans.com/809027115/webrtc and Adrià is open to meetings but we
don't know his schedule after his talk yet, if you're interested in meeting
with him, shoot me an email

For great research discussions, look no further than this talk!
Michael

*TITLE* Where are they looking?

*KEYWORDS *computer vision

*ABSTRACT*
Humans have the remarkable ability to follow the gaze of other people to
identify what they are looking at. Following eye gaze, or gaze-following,
is an important ability that allows us to understand what other people are
thinking, the actions they are performing, and even predict what they might
do next. Despite the importance of this topic, this problem has only been
studied in limited scenarios within the computer vision community. In this
talk I will present a deep neural network-based approach for
gaze-following. Given an image and the location of a head, our approach
follows the gaze of the person and identifies the object being looked at.
Furthermore, I will also introduce GazeNet, a deep neural-network to
predict the 3D direction of a person's gaze from the full 360 degrees. To
complement GazeNet, I will present a novel saliency-based sampling layer
for neural networks, the Saliency Sampler, which helps to improve the
spatial sampling of input data for an arbitrary task. Our differentiable
layer can be added as a preprocessing block to existing task networks and
trained altogether in an end-to-end fashion. The effect of the layer is to
efficiently estimate how to sample from the original data in order to boost
task performance. For example, for the gaze-tracking task in which the
original data might range in size up to several megapixels, but where the
desired input images to the task network are much smaller, our layer learns
how best to sample from the underlying high resolution data in a manner
which preserves task-relevant information better than uniform downsampling.

*BIO*
You can read his bio here: https://people.csail.mit.edu/recasens/
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