14 citations · 16 across the 7 of their papers we have counts for
12 papers
Residual Learning for Image Point Descriptors
Rashik Shrestha, Ajad Chhatkuli, Menelaos Kanakis +1
Local image feature descriptors have had a tremendous impact on the development and application of computer vision methods. It is therefore unsurprising that significant efforts ar…
Long-Term Invariant Local Features via Implicit Cross-Domain Correspondences
Zador Pataki, Mohammad Altillawi, Menelaos Kanakis +5
Modern learning-based visual feature extraction networks perform well in intra-domain localization, however, their performance significantly declines when image pairs are captured…
Breathing New Life into 3D Assets with Generative Repainting
Tianfu Wang, Menelaos Kanakis, Konrad Schindler +2
Diffusion-based text-to-image models ignited immense attention from the vision community, artists, and content creators. Broad adoption of these models is due to significant improv…
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…
ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization
Menelaos Kanakis, Simon Maurer, Matteo Spallanzani +2
Efficient detection and description of geometric regions in images is a prerequisite in visual systems for localization and mapping. Such systems still rely on traditional hand-cra…
Efficient Visual Tracking with Exemplar Transformers
Philippe Blatter, Menelaos Kanakis, Martin Danelljan +1
The design of more complex and powerful neural network models has significantly advanced the state-of-the-art in visual object tracking. These advances can be attributed to deeper…
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…
Automated Search for Resource-Efficient Branched Multi-Task Networks
David Bruggemann, Menelaos Kanakis, Stamatios Georgoulis +1
The multi-modal nature of many vision problems calls for neural network architectures that can perform multiple tasks concurrently. Typically, such architectures have been handcraf…
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…
T-Basis: a Compact Representation for Neural Networks
Anton Obukhov, Maxim Rakhuba, Stamatios Georgoulis +3
We introduce T-Basis, a novel concept for a compact representation of a set of tensors, each of an arbitrary shape, which is often seen in Neural Networks. Each of the tensors in t…
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…