Full paper: http://arxiv.org/abs/1412.1897
// This is clever. It's funny that they draw a lesson about how easy it is to fool these DNNs, since the same technique could be used as a method for machines to talk to each other while evading human detection.
“Two pizzas sitting on top of a stove top oven”
“A group of people shopping at an outdoor market”
“Best seats in the house”
People can summarize a complex scene in a few words without thinking twice. It’s much more difficult for computers. But we’ve just gotten a bit closer -- we’ve developed a machine-learning system that can automatically produce captions (like the three above) to accurately describe images the first time it sees them.
By constructing a model that combines a vision Convolutional Neural Network (CNN) with a language-generating Recurrent Neural Network (RNN), the system can take an image and generate a fitting natural-language caption. Head over to the Google Research blog to learn more.
- Vrije Universiteit BrusselMaster Computer Science, 2000 - 2004
- Vrije Universiteit BrusselPhD. Computer Science, 2004 - 2010
- AI-Lab (VUB)Post-Doctoral researcher, 2010 - present-Machine learning / Reinforcement Learning research -Coordinate research projects -Funding applications -Supervise Master & PhD students
- Vrije Universiteit Brussel -Department Computer ScienceDoctor Assistant, 2010 - presentTeaching courses & exercises Bachelor Computer Science & Master Computer Science - AI specialization
- Computational Modeling Lab (VUB)Post-Doctoral researcher, 2010 - 2010Researcher on project: Learning Control for Production Machines (lecopro.org)
- Computational Modeling Lab (VUB)researcher, 2004 - 2010Phd Student & researcher on project Multi-type Ant Colony Optimization
- MICC-IKATvisiting researcher, 2007 - 2007Visiting researcher MICC lab Maastricht University
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