1 code implementation • 19 Feb 2024 • James Oldfield, Markos Georgopoulos, Grigorios G. Chrysos, Christos Tzelepis, Yannis Panagakis, Mihalis A. Nicolaou, Jiankang Deng, Ioannis Patras
The Mixture of Experts (MoE) paradigm provides a powerful way to decompose inscrutable dense layers into smaller, modular computations often more amenable to human interpretation, debugging, and editability.
no code implementations • 14 Feb 2024 • Yixin Cheng, Markos Georgopoulos, Volkan Cevher, Grigorios G. Chrysos
We contend that the prior context--the information preceding the attack query--plays a pivotal role in enabling potent Jailbreaking attacks.
no code implementations • 31 Jan 2024 • Yixin Cheng, Grigorios G. Chrysos, Markos Georgopoulos, Volkan Cevher
On the other hand, Polynomial Networks is a class of models that does not require activation functions, but have yet to perform on par with modern architectures.
no code implementations • 11 Dec 2023 • Simon Giebenhain, Tobias Kirschstein, Markos Georgopoulos, Martin Rünz, Lourdes Agapito, Matthias Nießner
We present Monocular Neural Parametric Head Models (MonoNPHM) for dynamic 3D head reconstructions from monocular RGB videos.
1 code implementation • 10 May 2023 • Mustafa Işık, Martin Rünz, Markos Georgopoulos, Taras Khakhulin, Jonathan Starck, Lourdes Agapito, Matthias Nießner
To close the gap to production-level quality, we introduce HumanRF, a 4D dynamic neural scene representation that captures full-body appearance in motion from multi-view video input, and enables playback from novel, unseen viewpoints.
no code implementations • CVPR 2023 • Simon Giebenhain, Tobias Kirschstein, Markos Georgopoulos, Martin Rünz, Lourdes Agapito, Matthias Nießner
We propose a novel 3D morphable model for complete human heads based on hybrid neural fields.
no code implementations • CVPR 2022 • Markos Georgopoulos, James Oldfield, Grigorios G Chrysos, Yannis Panagakis
The results highlight the ability of our approach to condition image generation on attributes like gender, pose and hair style on faces, as well as a variety of features on different object classes.
1 code implementation • NeurIPS 2021 • Grigorios Chrysos, Markos Georgopoulos, Yannis Panagakis
We exhibit how CoPE can be trivially augmented to accept an arbitrary number of input variables.
no code implementations • 23 Nov 2021 • James Oldfield, Markos Georgopoulos, Yannis Panagakis, Mihalis A. Nicolaou, Ioannis Patras
This paper addresses the problem of finding interpretable directions in the latent space of pre-trained Generative Adversarial Networks (GANs) to facilitate controllable image synthesis.
2 code implementations • 16 Apr 2021 • Grigorios G Chrysos, Markos Georgopoulos, Jiankang Deng, Jean Kossaifi, Yannis Panagakis, Anima Anandkumar
The efficacy of the proposed models is evaluated on standard image and audio classification benchmarks.
Ranked #2 on Audio Classification on Speech Commands
1 code implementation • 11 Apr 2021 • Grigorios G Chrysos, Markos Georgopoulos, Yannis Panagakis
We exhibit how CoPE can be trivially augmented to accept an arbitrary number of input variables.
no code implementations • ICML 2020 • Markos Georgopoulos, Grigorios Chrysos, Maja Pantic, Yannis Panagakis
Deep generative models rely on their inductive bias to facilitate generalization, especially for problems with high dimensional data, like images.
no code implementations • 6 Jun 2020 • Markos Georgopoulos, James Oldfield, Mihalis A. Nicolaou, Yannis Panagakis, Maja Pantic
By evaluating on several age-annotated datasets in both single- and cross-database experiments, we show that the proposed method outperforms state-of-the-art algorithms for age transfer, especially in the case of age groups that lie in the tails of the label distribution.
no code implementations • 15 May 2020 • Markos Georgopoulos, Yannis Panagakis, Maja Pantic
In this work, we investigate the demographic bias of deep learning models in face recognition, age estimation, gender recognition and kinship verification.
no code implementations • 13 Feb 2018 • Markos Georgopoulos, Yannis Panagakis, Maja Pantic
Computational facial models that capture properties of facial cues related to aging and kinship increasingly attract the attention of the research community, enabling the development of reliable methods for age progression, age estimation, age-invariant facial characterization, and kinship verification from visual data.