5 papers
Composite Learning for Robust and Effective Dense Predictions
Menelaos Kanakis, Thomas E. Huang, David Bruggemann +2
Multi-task learning promises better model generalization on a target task by jointly optimizing it with an auxiliary task. However, the current practice requires additional labelin…
Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation
Suman Saha, Anton Obukhov, Danda Pani Paudel +4
We present an approach for encoding visual task relationships to improve model performance in an Unsupervised Domain Adaptation (UDA) setting. Semantic segmentation and monocular d…
Exploring Relational Context for Multi-Task Dense Prediction
David Bruggemann, Menelaos Kanakis, Anton Obukhov +2
The timeline of computer vision research is marked with advances in learning and utilizing efficient contextual representations. Most of them, however, are targeted at improving mo…
Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference
Menelaos Kanakis, David Bruggemann, Suman Saha +3
Multi-task networks are commonly utilized to alleviate the need for a large number of highly specialized single-task networks. However, two common challenges in developing multi-ta…
Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing
Suman Saha, Wenhao Xu, Menelaos Kanakis +4
Nowadays, the increasingly growing number of mobile and computing devices has led to a demand for safer user authentication systems. Face anti-spoofing is a measure towards this di…