There has been a *time change *for this talk to *12:45PM*, the room is still in *AA3195*
Also, if you're interested in meeting with Adrià, he'll be free 2pm - 3pm. Sign up here: https://calendar.google.com/calendar/selfsched?sstoken=UVBVTVF0X25Nd09LfGRlZ...
Sorry for the last minute changes, big shout out to Dima B. for arranging everything and keeping it running smoothly!
On Wed, May 23, 2018 at 4:21 PM Michael Noukhovitch mnoukhov@gmail.com wrote:
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/