My research goal is to find a bridge between prior knowledge and deep networks. Deep learning in computer vision thrives under examples but commonly ignores additional explicit knowledge about the research problem. Think about hierarchical relations between categories, relational knowledge between tasks, or spatio-temporal knowledge. The research of my team focuses on discovering the shared geometry between pixels and knowledge. Specifically, we research hyperbolic and hyperspherical geometry for deep learning and learning with prototypes. Our advances and new research proposals are investigated for computer vision problems such as hierarchical recognition, segmentation, localization, and zero-shot recognition in images and videos.