[Lisa_seminaires] UdeM-McGill-MITACS machine learning seminar Fri Nov. 2, 11:30am, MC 437

Hugo Larochelle larocheh at iro.umontreal.ca
Mar 30 Oct 18:10:00 EDT 2007


This week's seminar (see http://www.iro.umontreal.ca/article.php3?
id_article=107&lang=en):

Preliminary steps toward a probabilistic, decision theoretic
model of dynamic scene understanding

by Nando de Freitas
Computer Science Department
University of british Columbia

Location: Mc Gill, McConnell engineering building room 437
Time: November 2nd 2007, 11:30am

Abstract:

It is widely agreed that efficient visual search requires the
integration of target-driven top-down information and image-driven
bottom-up information. Yet the problem of gaze planning - that is,
selecting the next best gaze location given the current observations -
remains largely unsolved. We propose a probabilistic system that models
the gaze sequence as a finite-horizon Bayesian sequential decision
process. Direct policy search is used to reason about the next best gaze
locations. The system integrates bottom-up saliency information,
top-down target knowledge and additional context information through
principled Bayesian priors. This results in proposal gaze locations that
depend not only the feature visual saliency, but also on prior knowledge
and the spatial likelihood of locating the target. The system has been
implemented using state-of-the-art object detectors and evaluated on a
real-world dataset by comparing it to gaze sequences proposed by a pure
bottom-up saliency-based process and to an object detection approach
that analyzes the full image. The target-directed attention system is
shown to result in higher object detection precision than both
competitors, to attend to more relevant targets than the bottom-up
attention system, and to require significantly less computation time
than the exhaustive approach.

This is joint work with Julia Vogel.



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