Campus Event Calendar
Event Entry
What and Who
Prediction of search targets from fixations in open-world settings
Hosnieh Sattar
International Max Planck Research School for Computer Science - IMPRS
IMPRS Research Seminar
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI
Public Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Monday, 23 November 2015
12:05
45 Minutes
E1 4
024
Saarbrücken
Abstract
Previous work on predicting the target of visual search from human
fixations only considered closed-world settings in which training labels
are available and predictions are performed for a known set of potential
targets. In this work we go beyond the state of the art by studying search
target prediction in an open-world setting in which we no longer assume
that we have fixation data to train for the search targets. We present a
dataset containing fixation data of 18 users searching for natural images
from three image categories within synthesised image collages of about 80
images. In a closed-world baseline experiment we show that we can predict
the correct target image out of a candidate set of five images. We then
present a new problem formulation for search target prediction in the
open-world setting that is based on learning compatibilities between
fixations and potential targets.
fixations only considered closed-world settings in which training labels
are available and predictions are performed for a known set of potential
targets. In this work we go beyond the state of the art by studying search
target prediction in an open-world setting in which we no longer assume
that we have fixation data to train for the search targets. We present a
dataset containing fixation data of 18 users searching for natural images
from three image categories within synthesised image collages of about 80
images. In a closed-world baseline experiment we show that we can predict
the correct target image out of a candidate set of five images. We then
present a new problem formulation for search target prediction in the
open-world setting that is based on learning compatibilities between
fixations and potential targets.
http://perceptual.mpi-inf.mpg.de/files/2015/04/sattar15_cvpr.pdf
Contact
Andrea Ruffing
--email hidden
passcode not visible
logged in users only
Andrea Ruffing, 11/18/2015 11:55 -- Created document.