Visual data: abundant, relevant, labelled, cheap. Pick two?


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In Computer Vision, we often find data is either never enough, or expensive to label, or not relevant to the problem. There isn’t an Imagenet for every sensor. What tools can you use on little (or no) visual data, what purpose do they serve and what risks do they have?


Irina Vidal Migallon

Electrical Engineer & Biomedical Engineer who specialised in Machine Learning & Vision. Seasoned in different industries - from optical biopsy systems in France to surgical planning tools and Augmented Reality apps in the Berlin start-up scene-, she now works in Siemens Mobility’s Computer Vision & AI team. Even more than waking up Skynet, she’s interested in the limits of Natural Intelligence and its decisions over our data.