3 citations · 4 across the 8 of their papers we have counts for
8 papers
PrismAvatar: Real-time animated 3D neural head avatars on edge devices
Prashant Raina, Felix Taubner, Mathieu Tuli +2
We present PrismAvatar: a 3D head avatar model which is designed specifically to enable real-time animation and rendering on resource-constrained edge devices, while still enjoying…
3D Face Tracking from 2D Video through Iterative Dense UV to Image Flow
Felix Taubner, Prashant Raina, Mathieu Tuli +3
When working with 3D facial data, improving fidelity and avoiding the uncanny valley effect is critically dependent on accurate 3D facial performance capture. Because such methods…
Embracing Annotation Efficient Learning (AEL) for Digital Pathology and Natural Images
Eu Wern Teh
Jitendra Malik once said, "Supervision is the opium of the AI researcher". Most deep learning techniques heavily rely on extreme amounts of human labels to work effectively. In tod…
Understanding the impact of image and input resolution on deep digital pathology patch classifiers
Eu Wern Teh, Graham W. Taylor
We consider annotation efficient learning in Digital Pathology (DP), where expert annotations are expensive and thus scarce. We explore the impact of image and input resolution on…
Learning with Less Labels in Digital Pathology via Scribble Supervision from Natural Images
Eu Wern Teh, Graham W. Taylor
A critical challenge of training deep learning models in the Digital Pathology (DP) domain is the high annotation cost by medical experts. One way to tackle this issue is via trans…
The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation
Eu Wern Teh, Terrance DeVries, Brendan Duke +3
We consider the task of semi-supervised semantic segmentation, where we aim to produce pixel-wise semantic object masks given only a small number of human-labeled training examples…
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
Eu Wern Teh, Terrance DeVries, Graham W. Taylor
We consider the problem of distance metric learning (DML), where the task is to learn an effective similarity measure between images. We revisit ProxyNCA and incorporate several en…
Learning with less data via Weakly Labeled Patch Classification in Digital Pathology
Eu Wern Teh, Graham W. Taylor
In Digital Pathology (DP), labeled data is generally very scarce due to the requirement that medical experts provide annotations. We address this issue by learning transferable fea…