Google Research Grant for End-to-End Active Learning Framework for Medical Image Annotation

We develop a modularized active learning framework within the Google Cloud Platform, facilitating large-scale medical image annotation in a cost-effective manner while ensuring data sovereignty and privacy. Our work emphasizes a federated learning use case for healthcare data, taking into consideration data protection and security aspects. Our goal is to Read more…

Interactive annotation of passive acoustic monitoring datasets

Passive acoustic monitoring (PAM), the recording of sounds using microphones (e.g. in biosphere reserves), is an increasingly popular method for continuous, reproducible, scalable and cost-effective monitoring of wildlife [Sugai et al., 2018]. It is widely employed in various fields, including ecology, marine biology, and conservation, to study animal behavior, biodiversity, Read more…

Error-Aware Gaze-Based Interfaces for Robust Mobile Gaze Interaction

Gaze estimation error can severely hamper usability and performance of mobile gaze-based interfaces given that the error varies constantly for different interaction positions.┬áIn this work, we explore error-aware gaze-based interfaces that estimate and adapt to gaze estimation error on-the-fly. We implement a sample error-aware user interface for gaze-based selection and Read more…

Modelling Visual Attention for Generating an Artificial Episodic Memory

Visual Attention and the Artificial Episodic Memory Recent advances in mobile eye tracking technologies opened the way to design novel attention-aware intelligent user interaction. In particular, the gaze signal can be used to create or improve artificial episodic memories for offline processing, but also real-time processing. We investigate different methods Read more…