Abstract
Previous experiments have shown that human attention is influenced by high level task demands. In this paper, we propose an architecture to estimate the task-relevance of attended locations in a scene.We maintain a task graph and compute relevance of fixations using an ontology that contains a description of real world entities and their relationships. Our model guides attention according to a topographic attention guidance map that encodes the bottom-up salience and task-relevance of all locations in the scene.We have demonstrated that our model detects entities that are salient and relevant to the task even on natural cluttered scenes and arbitrary tasks.
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Navalpakkam, V., Itti, L. (2002). A Goal Oriented Attention Guidance Model. In: Bülthoff, H.H., Wallraven, C., Lee, SW., Poggio, T.A. (eds) Biologically Motivated Computer Vision. BMCV 2002. Lecture Notes in Computer Science, vol 2525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36181-2_45
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DOI: https://doi.org/10.1007/3-540-36181-2_45
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