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
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
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In the field of artificial intelligence, there is a considerable interest in the notion of sensory-motor coordination as an explanation for intelligent behaviour. However, there has been little research on sensory-motor coordination in tasks that go beyond low-level behavioural tasks. In this paper we show that sensory-motor coordination can also enhance performance on a high-level task: artificial gaze control for gender recognition in natural images. To investigate the advantage of sensory-motor coordination, we compare a non-situated model of gaze control (incapable of sensory-motor coordination) with a situated model of gaze control (capable of sensory-motor coordination). The non-situated model of gaze control shifts the gaze according to a fixed set of locations, optimised by an evolutionary algorithm. The situated model of gaze control determines gaze shifts on the basis of local inputs in a visual scene. An evolutionary algorithm optimises the model's gaze control policy. From the experiments performed, we may conclude that sensory-motor coordination contributes to artificial gaze control for the high-level task of gender recognition in natural images: the situated model outperforms the non-situated model. The mechanism of sensory-motor coordination establishes dependencies between multiple actions and observations that are exploited to optimise categorisation performance.