Abstract
We introduce a fully autonomous active vision system that explores its environment and learns visual representations of objects in the scene. The system design is motivated by the fact that infants learn internal representations of the world without much human assistance. Inspired by this, we build a curiosity driven system that is drawn towards locations in the scene that provide the highest potential for learning. In particular, the attention on a stimulus in the scene is related to the improvement in its internal model. This makes the system learn dynamic changes of object appearance in a cumulative fashion. We also introduce a self-correction mechanism in the system that rectifies situations where several distinct models have been learned for the same object or a single model has been learned for adjacent objects. We demonstrate through experiments that the curiosity-driven learning leads to a higher learning speed and improved accuracy.
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Acknowledgements
This work was supported by the BMBF Project “Bernstein Fokus: Neurotechnologie Frankfurt, FKZ 01GQ0840” and by the “IM-CLeVeR - Intrinsically Motivated Cumulative Learning Versatile Robots” project, FP7-ICT-IP-231722. We thank Richard Veale, Indiana University for providing the code on saliency.
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Chandrashekhariah, P., Spina, G., Triesch, J. (2014). A Curious Vision System for Autonomous and Cumulative Object Learning. In: Battiato, S., Coquillart, S., Laramee, R., Kerren, A., Braz, J. (eds) Computer Vision, Imaging and Computer Graphics -- Theory and Applications. VISIGRAPP 2013. Communications in Computer and Information Science, vol 458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44911-0_13
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