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A. Borji, D. N. Sihite, L. Itti, Probabilistic Learning of Task-Specific Visual Attention, In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, Rhode Island, pp. 1-8, Jun 2012. [2012 acceptance rate: 26.2%] (Cited by 88)
Abstract: Despite a considerable amount of previous work on bottom-up saliency modeling for predicting human fixations over static and dynamic stimuli, few studies have thus far attempted to model top-down and task-driven influences of visual attention. Here, taking advantage of the sequential nature of real-world tasks, we propose a unified Bayesian approach for modeling task-driven visual attention. Several sources of information, including global context of a scene, previous attended locations, and previous motor actions, are integrated over time to predict the next attended location. Recording eye movements while subjects engage in 5 contemporary 2D and 3D video games, as modest counterparts of everyday tasks, we show that our approach is able to predict human attention and gaze better than the state-of-the-art, with a large margin (about 15 percent increase in prediction accuracy). The advantage of our approach is that it is automatic and applicable to arbitrary visual tasks.
Themes: Model of Bottom-Up Saliency-Based Visual Attention, Model of Top-Down Attentional Modulation, Computational Modeling, Computer Vision
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