We're releasing a state-of-the-art multilingual and multi-domain neural machine translation model specialised for biomedical data that enables translation into English from five languages (French, German, Italian, Spanish and Korean).
A new platform based on deep-learning approaches to handwritten-text recognition and information extraction enables data from century-old documents to be parsed and analysed, making it possible to explore epidemics and the evolution of populations over time.
Evaluating our method on public datasets, we show that it can successfully solve challenging situations in dynamic environments which cause state-of-the-art baseline VSLAM algorithms to fail and that it maintains performance on static scenes.
A new double depth-map representation of the human shape allows to recover 3D details from a single image. Using 2 depth maps (visible and hidden) makes representations more efficient and easier to handle.