Niloofar Azizi — accepted AI-conference papers

2026

Spectral Basis Learning for Expressive Graph Neural Networks in Link Prediction

Niloofar Azizi, Nils M. Kriege, Nicholas J. A. Harvey, Horst Bischof

AAAI 2026technical

Graph Neural Networks (GNNs) excel in handling graph-structured data but often underperform in link prediction tasks compared to classical methods, mainly due to the limitations of the commonly used message-passing principle. Notably, their ability to distinguish non-isomorphic graphs is limited by

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2022

3D Human Pose Estimation Using Möbius Graph Convolutional Networks

Niloofar Azizi, Horst Possegger, Emanuele Rodolà, Horst Bischof

ECCV 2022poster

"3D human pose estimation is fundamental to understanding human behavior. Recently, promising results have been achieved by graph convolutional networks(GCNs), which achieve state-of-the-art performance and provide rather light-weight architectures. However, a major limitation of GCNs is their inabi…

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