47 citations · 147 across the 23 of their papers we have counts for
23 papers
CycleCap: Improving VLMs Captioning Performance via Self-Supervised Cycle Consistency Fine-Tuning
Marios Krestenitis, Christos Tzelepis, Konstantinos Ioannidis +5
Visual-Language Models (VLMs) have achieved remarkable progress in image captioning, visual question answering, and visual reasoning. Yet they remain prone to vision-language misal…
Multi-scale Image Super Resolution with a Single Auto-Regressive Model
Enrique Sanchez, Isma Hadji, Adrian Bulat +3
In this paper we tackle Image Super Resolution (ISR), using recent advances in Visual Auto-Regressive (VAR) modeling. VAR iteratively estimates the residual in latent space between…
MM2Latent: Text-to-facial image generation and editing in GANs with multimodal assistance
Debin Meng, Christos Tzelepis, Ioannis Patras +1
Generating human portraits is a hot topic in the image generation area, e.g. mask-to-face generation and text-to-face generation. However, these unimodal generation methods lack co…
Are CLIP features all you need for Universal Synthetic Image Origin Attribution?
Dario Cioni, Christos Tzelepis, Lorenzo Seidenari +1
The steady improvement of Diffusion Models for visual synthesis has given rise to many new and interesting use cases of synthetic images but also has raised concerns about their po…
DiffusionAct: Controllable Diffusion Autoencoder for One-shot Face Reenactment
Stella Bounareli, Christos Tzelepis, Vasileios Argyriou +2
Video-driven neural face reenactment aims to synthesize realistic facial images that successfully preserve the identity and appearance of a source face, while transferring the targ…
One-shot Neural Face Reenactment via Finding Directions in GAN's Latent Space
Stella Bounareli, Christos Tzelepis, Vasileios Argyriou +2
In this paper, we present our framework for neural face/head reenactment whose goal is to transfer the 3D head orientation and expression of a target face to a source face. Previou…
Multilinear Mixture of Experts: Scalable Expert Specialization through Factorization
James Oldfield, Markos Georgopoulos, Grigorios G. Chrysos +5
The Mixture of Experts (MoE) paradigm provides a powerful way to decompose dense layers into smaller, modular computations often more amenable to human interpretation, debugging, a…
Improving Fairness using Vision-Language Driven Image Augmentation
Moreno D'Incà, Christos Tzelepis, Ioannis Patras +1
Fairness is crucial when training a deep-learning discriminative model, especially in the facial domain. Models tend to correlate specific characteristics (such as age and skin col…
HyperReenact: One-Shot Reenactment via Jointly Learning to Refine and Retarget Faces
Stella Bounareli, Christos Tzelepis, Vasileios Argyriou +2
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…
Parts of Speech-Grounded Subspaces in Vision-Language Models
James Oldfield, Christos Tzelepis, Yannis Panagakis +2
Latent image representations arising from vision-language models have proved immensely useful for a variety of downstream tasks. However, their utility is limited by their entangle…
Self-Supervised Video Similarity Learning
Giorgos Kordopatis-Zilos, Giorgos Tolias, Christos Tzelepis +3
We introduce SVS, a video similarity learning approach with self-supervision. Self-Supervised Learning (SSL) is typically used to train deep models on a proxy task so as to hav…
Attribute-preserving Face Dataset Anonymization via Latent Code Optimization
Simone Barattin, Christos Tzelepis, Ioannis Patras +1
This work addresses the problem of anonymizing the identity of faces in a dataset of images, such that the privacy of those depicted is not violated, while at the same time the dat…
StyleMask: Disentangling the Style Space of StyleGAN2 for Neural Face Reenactment
Stella Bounareli, Christos Tzelepis, Vasileios Argyriou +2
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 he…
ContraCLIP: Interpretable GAN generation driven by pairs of contrasting sentences
Christos Tzelepis, James Oldfield, Georgios Tzimiropoulos +1
This work addresses the problem of discovering non-linear interpretable paths in the latent space of pre-trained GANs in a model-agnostic manner. In the proposed method, the discov…
PandA: Unsupervised Learning of Parts and Appearances in the Feature Maps of GANs
James Oldfield, Christos Tzelepis, Yannis Panagakis +2
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 seman…
WarpedGANSpace: Finding non-linear RBF paths in GAN latent space
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…
DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval
Giorgos Kordopatis-Zilos, Christos Tzelepis, Symeon Papadopoulos +2
In this paper, we address the problem of high performance and computationally efficient content-based video retrieval in large-scale datasets. Current methods typically propose eit…
Few-Shot Action Localization without Knowing Boundaries
Ting-Ting Xie, Christos Tzelepis, Fan Fu +1
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…
Uncertainty Propagation in Convolutional Neural Networks: Technical Report
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…
Temporal Action Localization with Variance-Aware Networks
Ting-Ting Xie, Christos Tzelepis, Ioannis Patras
This work addresses the problem of temporal action localization with Variance-Aware Networks (VAN), i.e., DNNs that use second-order statistics in the input and/or the output of re…
Boundary Uncertainty in a Single-Stage Temporal Action Localization Network
Ting-Ting Xie, Christos Tzelepis, Ioannis Patras
In this paper, we address the problem of temporal action localization with a single stage neural network. In the proposed architecture we model the boundary predictions as uni-vari…
Learning to detect video events from zero or very few video examples
Christos Tzelepis, Damianos Galanopoulos, Vasileios Mezaris +1
In this work we deal with the problem of high-level event detection in video. Specifically, we study the challenging problems of i) learning to detect video events from solely a te…
Linear Maximum Margin Classifier for Learning from Uncertain Data
Christos Tzelepis, Vasileios Mezaris, Ioannis Patras
In this paper, we propose a maximum margin classifier that deals with uncertainty in data input. More specifically, we reformulate the SVM framework such that each training example…