Task-dependent learning of attention
Neural Networks
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attentional Selection for Object Recognition A Gentle Way
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Visual attention based information culling for Distributed Virtual Environments
Proceedings of the ACM symposium on Virtual reality software and technology
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Evaluation of selective attention under similarity transformations
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Visually directing user's attention in interactive 3D environments
SIGGRAPH '04 ACM SIGGRAPH 2004 Posters
Incremental Knowledge Representation Based on Visual Selective Attention
Neural Information Processing
Real-world vision: Selective perception and task
ACM Transactions on Applied Perception (TAP)
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Evaluation of selective attention under similarity transformations
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Implementation of visual attention system using bottom-up saliency map model
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
A biologically-inspired vision architecture for resource-constrained intelligent vehicles
Computer Vision and Image Understanding
A biologically inspired object-based visual attention model
Artificial Intelligence Review
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
“What” and “where” information based attention guidance model
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Cognition theory based performance characterization in computer vision
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Performance characterization in computer vision: the role of visual cognition theory
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Generic solution for image object recognition based on vision cognition theory
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Top-down attention guided object detection
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
A system to support attention allocation: Development and application
Web Intelligence and Agent Systems
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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.