244 citations · 824 across the 56 of their papers we have counts for
27 papers · 1 filter
Sparsity-Inducing Divergence Losses for Biometric Verification
Dimitrios Koutsianos, Ladislav Mošner, Yannis Panagakis +1
Performance in face and speaker verification is largely driven by margin-penalty softmax losses such as CosFace and ArcFace. Recently introduced -divergence loss functions offer…
GATE-AD: Graph Attention Network Encoding For Few-Shot Industrial Visual Anomaly Detection
Aggelos Psiris, Yannis Panagakis, Maria Vakalopoulou +1
Few-Shot Industrial Visual Anomaly Detection (FS-IVAD) comprises a critical task in modern manufacturing settings, where automated product inspection systems need to identify rare…
Alpha Divergence Losses for Biometric Verification
Dimitrios Koutsianos, Ladislav Mosner, Yannis Panagakis +1
Performance in face and speaker verification is largely driven by margin-based softmax losses such as CosFace and ArcFace. Recently introduced -divergence loss functions offer a…
Environment-Aware Satellite Image Generation with Diffusion Models
Nikos Kostagiolas, Pantelis Georgiades, Yannis Panagakis +1
Diffusion-based foundation models have recently garnered much attention in the field of generative modeling due to their ability to generate images of high quality and fidelity. Al…
Causally Steered Diffusion for Automated Video Counterfactual Generation
Nikos Spyrou, Athanasios Vlontzos, Paraskevas Pegios +5
Adapting text-to-image (T2I) latent diffusion models (LDMs) to video editing has shown strong visual fidelity and controllability, but challenges remain in maintaining causal relat…
Enabling Local Editing in Diffusion Models by Joint and Individual Component Analysis
Theodoros Kouzelis, Manos Plitsis, Mihalis A. Nicolaou +1
Recent advances in Diffusion Models (DMs) have led to significant progress in visual synthesis and editing tasks, establishing them as a strong competitor to Generative Adversarial…
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…
Locality-preserving Directions for Interpreting the Latent Space of Satellite Image GANs
Georgia Kourmouli, Nikos Kostagiolas, Yannis Panagakis +1
We present a locality-aware method for interpreting the latent space of wavelet-based Generative Adversarial Networks (GANs), that can well capture the large spatial and spectral v…
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…
Unsupervised Discovery of Semantic Concepts in Satellite Imagery with Style-based Wavelet-driven Generative Models
Nikos Kostagiolas, Mihalis A. Nicolaou, Yannis Panagakis
In recent years, considerable advancements have been made in the area of Generative Adversarial Networks (GANs), particularly with the advent of style-based architectures that addr…
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…
Cluster-guided Image Synthesis with Unconditional Models
Markos Georgopoulos, James Oldfield, Grigorios G Chrysos +1
Generative Adversarial Networks (GANs) are the driving force behind the state-of-the-art in image generation. Despite their ability to synthesize high-resolution photo-realistic im…
Tensor Component Analysis for Interpreting the Latent Space of GANs
James Oldfield, Markos Georgopoulos, Yannis Panagakis +2
This paper addresses the problem of finding interpretable directions in the latent space of pre-trained Generative Adversarial Networks (GANs) to facilitate controllable image synt…
Tensor Methods in Computer Vision and Deep Learning
Yannis Panagakis, Jean Kossaifi, Grigorios G. Chrysos +4
Tensors, or multidimensional arrays, are data structures that can naturally represent visual data of multiple dimensions. Inherently able to efficiently capture structured, latent…
Augmenting Deep Classifiers with Polynomial Neural Networks
Grigorios G Chrysos, Markos Georgopoulos, Jiankang Deng +3
Deep neural networks have been the driving force behind the success in classification tasks, e.g., object and audio recognition. Impressive results and generalization have been ach…
Enhancing Facial Data Diversity with Style-based Face Aging
Markos Georgopoulos, James Oldfield, Mihalis A. Nicolaou +2
A significant limiting factor in training fair classifiers relates to the presence of dataset bias. In particular, face datasets are typically biased in terms of attributes such as…
Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Study
Markos Georgopoulos, Yannis Panagakis, Maja Pantic
Deep learning-based methods have pushed the limits of the state-of-the-art in face analysis. However, despite their success, these models have raised concerns regarding their bias…
Adversarial Learning of Disentangled and Generalizable Representations for Visual Attributes
James Oldfield, Yannis Panagakis, Mihalis A. Nicolaou
Recently, a multitude of methods for image-to-image translation have demonstrated impressive results on problems such as multi-domain or multi-attribute transfer. The vast majority…
Modeling of Facial Aging and Kinship: A Survey
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 developme…
Side Information for Face Completion: a Robust PCA Approach
Niannan Xue, Jiankang Deng, Shiyang Cheng +2
Robust principal component analysis (RPCA) is a powerful method for learning low-rank feature representation of various visual data. However, for certain types as well as significa…
Visual Data Augmentation through Learning
Grigorios G. Chrysos, Yannis Panagakis, Stefanos Zafeiriou
The rapid progress in machine learning methods has been empowered by i) huge datasets that have been collected and annotated, ii) improved engineering (e.g. data pre-processing/nor…
Multi-Attribute Robust Component Analysis for Facial UV Maps
Stylianos Moschoglou, Evangelos Ververas, Yannis Panagakis +2
Recently, due to the collection of large scale 3D face models, as well as the advent of deep learning, a significant progress has been made in the field of 3D face alignment "in-th…
GAGAN: Geometry-Aware Generative Adversarial Networks
Jean Kossaifi, Linh Tran, Yannis Panagakis +1
Deep generative models learned through adversarial training have become increasingly popular for their ability to generate naturalistic image textures. However, aside from their te…
An Adversarial Neuro-Tensorial Approach For Learning Disentangled Representations
Mengjiao Wang, Zhixin Shu, Shiyang Cheng +3
Several factors contribute to the appearance of an object in a visual scene, including pose, illumination, and deformation, among others. Each factor accounts for a source of varia…