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Paper TuA310.2

Borji, Ali (University of Southern California (USC)), Itti, Laurent (University of Southern California), Sihite, Dicky (University of Southern California)

Modeling the Influence of Action on Spatial Attention in Visual Interactive Environments

Scheduled for presentation during the Interactive Session "Interactive Session TuA-3" (TuA310), Tuesday, May 15, 2012, 09:30−10:00, Ballroom D

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 11, 2017

Keywords Visual Learning, Computer Vision for Robotics and Automation, Biologically-Inspired Robots

Abstract

A large number of studies have been reported on top-down influences of visual attention. However, less progress have been made in understanding and modeling its mechanisms in real-world tasks. In this paper, we propose an approach for learning spatial attention taking into account influences of physical actions on top-down attention. For this purpose, we focus on interactive visual environments (video games) which are modest real-world simulations, where a player has to attend to certain aspects of visual stimuli and perform actions to achieve a goal. The basic idea is to learn a mapping from current mental state of the game player, represented by past actions and observations, to its gaze fixation. A data-driven approach is followed where we train a model from the data of some players and test it over a new subject. In particular, two contributions this paper makes are: 1) employing multimodal information including mean eye position, gist of a scene, physical actions, bottom-up saliency, and tagged events for state representation and 2) analysis of different methods of combining bottom-up and top-down influences. Comparing with other top-down task-driven and bottom-up spatio-temporal models, our approach shows higher NSS scores in predicting eye positions.

 

 

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