Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Closed-Loop Object Recognition Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Understanding intelligence
ACM Computing Surveys (CSUR)
Learning Gender with Support Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Coevolution of active vision and feature selection
Biological Cybernetics
Performance scaling of multi-objective evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Comparing active vision models
Image and Vision Computing
Sub-sampling: Real-time vision for micro air vehicles
Robotics and Autonomous Systems
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This paper shows that sensory-motor coordination contributes to the performance of situated models on the high-level task of artificial gaze control for gender recognition in static natural images. To investigate the advantage of sensory-motor coordination, we compare a non-situated model of gaze control with a situated model. The non-situated model is incapable of sensory-motor coordination. It shifts the gaze according to a fixed set of locations, optimised by an evolutionary algorithm. The situated model determines gaze shifts on the basis of local inputs in a visual scene. An evolutionary algorithm optimises the model's gaze control policy. In the experiments performed, the situated model outperforms the non-situated model. By adopting a Bayesian framework, we show that the mechanism of sensory-motor coordination is the cause of this performance difference. The essence is that the mechanism maximises task-specific information in the observations over time, by establishing dependencies between multiple actions and observations.