1 code implementation • 28 May 2024 • Panagiotis Koromilas, Giorgos Bouritsas, Theodoros Giannakopoulos, Mihalis Nicolaou, Yannis Panagakis
DHEL simplifies the problem by decoupling the target hyperspherical energy from the alignment of positive examples while preserving the same theoretical guarantees.
no code implementations • 30 Sep 2021 • Rolandos Alexandros Potamias, Giorgos Bouritsas, Stefanos Zafeiriou
In an attempt to alleviate this computational burden, we propose a fast point cloud simplification method by learning to sample salient points.
1 code implementation • NeurIPS 2021 • Giorgos Bouritsas, Andreas Loukas, Nikolaos Karalias, Michael M. Bronstein
Can we use machine learning to compress graph data?
no code implementations • 10 Sep 2020 • Soha Sadat Mahdi, Nele Nauwelaers, Philip Joris, Giorgos Bouritsas, Shunwang Gong, Sergiy Bokhnyak, Susan Walsh, Mark D. Shriver, Michael Bronstein, Peter Claes, .
Since embeddings are accepted as an input, there is no need to train classifiers for the different properties and available data can be used more efficiently.
no code implementations • ECCV 2020 • Rolandos Alexandros Potamias, Jiali Zheng, Stylianos Ploumpis, Giorgos Bouritsas, Evangelos Ververas, Stefanos Zafeiriou
To this end, in this study we employ a deep mesh encoder-decoder like architecture to synthesize realistic high resolution facial expressions by using a single neutral frame along with an expression identification.
5 code implementations • 20 Jun 2020 • Grigorios Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Jiankang Deng, Yannis Panagakis, Stefanos Zafeiriou
We introduce three tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks.
Ranked #1 on Face Recognition on CALFW
2 code implementations • 16 Jun 2020 • Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, Michael M. Bronstein
It has been shown that the expressive power of standard GNNs is bounded by the Weisfeiler-Leman (WL) graph isomorphism test, from which they inherit proven limitations such as the inability to detect and count graph substructures.
Ranked #2 on Graph Regression on ZINC 100k
2 code implementations • 8 Mar 2020 • Grigorios G. Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Yannis Panagakis, Jiankang Deng, Stefanos Zafeiriou
Deep Convolutional Neural Networks (DCNNs) is currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning.
Ranked #1 on Graph Representation Learning on COMA
no code implementations • 12 Jul 2019 • Giorgos Bouritsas, Stelios Daveas, Antonios Danelakis, Constantinos Rizogiannis, Stelios C. A. Thomopoulos
Detection of anomalous trajectories is an important problem with potential applications to various domains, such as video surveillance, risk assessment, vessel monitoring and high-energy physics.
2 code implementations • ICCV 2019 • Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis, Michael Bronstein, Stefanos Zafeiriou
Generative models for 3D geometric data arise in many important applications in 3D computer vision and graphics.
1 code implementation • CVPR 2018 • Giorgos Bouritsas, Petros Koutras, Athanasia Zlatintsi, Petros Maragos
Despite the availability of a huge amount of video data accompanied by descriptive texts, it is not always easy to exploit the information contained in natural language in order to automatically recognize video concepts.