2017 |
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Antonio Lopez, Jiaolong Xu, Jose Luis Gomez, David Vazquez, & German Ros. (2017). From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example. In Domain Adaptation in Computer Vision Applications (pp. 243–258).
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2014 |
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Jiaolong Xu, David Vazquez, Antonio Lopez, Javier Marin, & Daniel Ponsa. (2014). Learning a Part-based Pedestrian Detector in Virtual World. IEEE Transactions on Intelligent Transportation Systems, 15, 2121–2131.
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David Vazquez, Javier Marin, Antonio Lopez, Daniel Ponsa, & David Geronimo. (2014). Virtual and Real World Adaptation for Pedestrian Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 797–809.
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2013 |
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Jiaolong Xu, David Vazquez, Antonio Lopez, Javier Marin, & Daniel Ponsa. (2013). Learning a Multiview Part-based Model in Virtual World for Pedestrian Detection. In IEEE Intelligent Vehicles Symposium (pp. 467–472).
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David Vazquez. (2013). Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection (Vol. 1). Ph.D. thesis, , .
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2012 |
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David Vazquez, Antonio Lopez, & Daniel Ponsa. (2012). Unsupervised Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection. In 21st International Conference on Pattern Recognition (pp. 3492–3495).
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Javier Marin, David Geronimo, David Vazquez, & Antonio Lopez. (2012). Pedestrian Detection: Exploring Virtual Worlds. In Handbook of Pattern Recognition: Methods and Application (Vol. 5, pp. 145–162).
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2011 |
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David Vazquez, Antonio Lopez, Daniel Ponsa, & Javier Marin. (2011). Virtual Worlds and Active Learning for Human Detection. In 13th International Conference on Multimodal Interaction (pp. 393–400).
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David Vazquez, Antonio Lopez, Daniel Ponsa, & Javier Marin. (2011). Cool world: domain adaptation of virtual and real worlds for human detection using active learning. In NIPS Domain Adaptation Workshop: Theory and Application.
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2010 |
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Javier Marin, David Vazquez, David Geronimo, & Antonio Lopez. (2010). Learning Appearance in Virtual Scenarios for Pedestrian Detection. In 23rd IEEE Conference on Computer Vision and Pattern Recognition (137–144).
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