no code implementations • 25 Mar 2024 • Stella Bounareli, Christos Tzelepis, Vasileios Argyriou, Ioannis Patras, Georgios Tzimiropoulos
To this end, in this paper we present DiffusionAct, a novel method that leverages the photo-realistic image generation of diffusion models to perform neural face reenactment.
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 • 5 Feb 2024 • Stella Bounareli, Christos Tzelepis, Vasileios Argyriou, Ioannis Patras, Georgios Tzimiropoulos
Moreover, we show that by embedding real images in the GAN latent space, our method can be successfully used for the reenactment of real-world faces.
no code implementations • 26 Nov 2023 • Sergio Naval Marimont, Matthew Baugh, Vasilis Siomos, Christos Tzelepis, Bernhard Kainz, Giacomo Tarroni
Such a score function is potentially relevant for UAD, since $\nabla_x \log p(x)$ is itself a pixel-wise anomaly score.
1 code implementation • 2 Nov 2023 • Moreno D'Incà, Christos Tzelepis, Ioannis Patras, Nicu Sebe
These paths are then applied to augment images to improve the fairness of a given dataset.
1 code implementation • ICCV 2023 • Stella Bounareli, Christos Tzelepis, Vasileios Argyriou, Ioannis Patras, Georgios Tzimiropoulos
In this paper, we present our method for neural face reenactment, called HyperReenact, that aims to generate realistic talking head images of a source identity, driven by a target facial pose.
2 code implementations • 23 May 2023 • James Oldfield, Christos Tzelepis, Yannis Panagakis, Mihalis A. Nicolaou, Ioannis Patras
Latent image representations arising from vision-language models have proved immensely useful for a variety of downstream tasks.
1 code implementation • 6 Apr 2023 • Giorgos Kordopatis-Zilos, Giorgos Tolias, Christos Tzelepis, Ioannis Kompatsiaris, Ioannis Patras, Symeon Papadopoulos
We introduce S$^2$VS, a video similarity learning approach with self-supervision.
Ranked #1 on Video Retrieval on FIVR-200K
1 code implementation • CVPR 2023 • Simone Barattin, Christos Tzelepis, Ioannis Patras, Nicu Sebe
By optimizing the latent codes directly, we ensure both that the identity is of a desired distance away from the original (with an identity obfuscation loss), whilst preserving the facial attributes (using a novel feature-matching loss in FaRL's deep feature space).
1 code implementation • 27 Sep 2022 • Stella Bounareli, Christos Tzelepis, Vasileios Argyriou, Ioannis Patras, Georgios Tzimiropoulos
In this paper we address the problem of neural face reenactment, where, given a pair of a source and a target facial image, we need to transfer the target's pose (defined as the head pose and its facial expressions) to the source image, by preserving at the same time the source's identity characteristics (e. g., facial shape, hair style, etc), even in the challenging case where the source and the target faces belong to different identities.
1 code implementation • 5 Jun 2022 • Christos Tzelepis, James Oldfield, Georgios Tzimiropoulos, Ioannis Patras
This work addresses the problem of discovering non-linear interpretable paths in the latent space of pre-trained GANs in a model-agnostic manner.
1 code implementation • 31 May 2022 • James Oldfield, Christos Tzelepis, Yannis Panagakis, Mihalis A. Nicolaou, Ioannis Patras
Recent advances in the understanding of Generative Adversarial Networks (GANs) have led to remarkable progress in visual editing and synthesis tasks, capitalizing on the rich semantics that are embedded in the latent spaces of pre-trained GANs.
1 code implementation • ICCV 2021 • Christos Tzelepis, Georgios Tzimiropoulos, Ioannis Patras
This work addresses the problem of discovering, in an unsupervised manner, interpretable paths in the latent space of pretrained GANs, so as to provide an intuitive and easy way of controlling the underlying generative factors.
1 code implementation • 24 Jun 2021 • Giorgos Kordopatis-Zilos, Christos Tzelepis, Symeon Papadopoulos, Ioannis Kompatsiaris, Ioannis Patras
In this work, we propose a Knowledge Distillation framework, called Distill-and-Select (DnS), that starting from a well-performing fine-grained Teacher Network learns: a) Student Networks at different retrieval performance and computational efficiency trade-offs and b) a Selector Network that at test time rapidly directs samples to the appropriate student to maintain both high retrieval performance and high computational efficiency.
Ranked #2 on Video Retrieval on FIVR-200K
1 code implementation • 8 Jun 2021 • Ting-Ting Xie, Christos Tzelepis, Fan Fu, Ioannis Patras
Learning to localize actions in long, cluttered, and untrimmed videos is a hard task, that in the literature has typically been addressed assuming the availability of large amounts of annotated training samples for each class -- either in a fully-supervised setting, where action boundaries are known, or in a weakly-supervised setting, where only class labels are known for each video.
2 code implementations • 11 Feb 2021 • Christos Tzelepis, Ioannis Patras
In this technical report we study the problem of propagation of uncertainty (in terms of variances of given uni-variate normal random variables) through typical building blocks of a Convolutional Neural Network (CNN).
no code implementations • 25 Aug 2020 • Ting-Ting Xie, Christos Tzelepis, Ioannis Patras
We use two uncertainty-aware boundary regression losses: first, the Kullback-Leibler divergence between the ground truth location of the boundary and the Gaussian modeling the prediction of the boundary and second, the expectation of the $\ell_1$ loss under the same Gaussian.
no code implementations • 25 Aug 2020 • Ting-Ting Xie, Christos Tzelepis, Ioannis Patras
Results in the action localization problem show that the incorporation of second order statistics improves over the baseline network, and that VANp surpasses the accuracy of virtually all other two-stage networks without involving any additional parameters.
no code implementations • 25 Nov 2015 • Christos Tzelepis, Damianos Galanopoulos, Vasileios Mezaris, Ioannis Patras
In this work we deal with the problem of high-level event detection in video.
1 code implementation • 15 Apr 2015 • Christos Tzelepis, Vasileios Mezaris, Ioannis Patras
In this paper, we propose a maximum margin classifier that deals with uncertainty in data input.