Maintaining Generic ConvNet Representation when Switching Data Type
Volumental is a Swedish computer vision company bringing 3D scanning and fitting solutions to footwear retailers around the world. To provide consumers with the most intelligent recommendations and engaging shopping assistance, one part of our product is about accurately measuring the consumers' feet. We use deep learning at various levels of our pipeline to represent, segment and classify visual and shape data. This talk will focus on how we have made use of powerful convolutional nets on data very different from what they were originally trained for, significanly reducing the annotation effort needed to reach high accuracy on new types of visual data.
Miroslav aqcuired his MSc in Optics & Photonics from Imperial College in 2008 and has since then worked with R&D in computer vision and machine learning in the industry and academia. He co-founded Volumental during his PhD in computer vision at KTH and is currently leading the company's deep learning efforts in building the world's best and most consumer centric sales assistant.