35 citations · 52 across the 11 of their papers we have counts for
13 papers
DF-CBM: Region-Aware Concept Bottleneck Models for Deepfake Detection
Georgios Tsoumplekas, Vazgken Vanian, Alexandros Doumanoglou +4
Deepfake detection methods have become increasingly effective yet most provide limited insight into the evidence behind their predictions. However, in forensic settings users also…
Why Fake ? Unveiling the Semantic Vocabulary of Deepfake Detectors
Vazgken Vanian, Alexandros Doumanoglou, Dimitris Zarpalas
Deepfake (DF) technology poses a significant threat to information integrity, driving the need for robust detection methods. Most DF detectors only consider predicting a binary lab…
Compressing What Matters: Neuron Importance Meets Data-Aware Low Rank Approximation for Language Model Compression
Athanasios Ntovas, Alexandros Doumanoglou, Petros Drakoulis +1
To excel at their domain large language models are comprised of billions of parameters. Yet this comes at the cost of huge memory requirements restricting their applicability in re…
Which Direction to Choose? An Analysis on the Representation Power of Self-Supervised ViTs in Downstream Tasks
Yannis Kaltampanidis, Alexandros Doumanoglou, Dimitrios Zarpalas
Self-Supervised Learning (SSL) for Vision Transformers (ViTs) has recently demonstrated considerable potential as a pre-training strategy for a variety of computer vision tasks, in…
Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks
Alexandros Doumanoglou, Kurt Driessens, Dimitrios Zarpalas
Empirical evidence shows that deep vision networks often represent concepts as directions in latent space with concept information written along directional components in the vecto…
Unsupervised Interpretable Basis Extraction for Concept-Based Visual Explanations
Alexandros Doumanoglou, Stylianos Asteriadis, Dimitrios Zarpalas
An important line of research attempts to explain CNN image classifier predictions and intermediate layer representations in terms of human-understandable concepts. Previous work s…
Cross-Stitched Multi-task Dual Recursive Networks for Unified Single Image Deraining and Desnowing
Sotiris Karavarsamis, Alexandros Doumanoglou, Konstantinos Konstantoudakis +1
We present the Cross-stitched Multi-task Unified Dual Recursive Network (CMUDRN) model targeting the task of unified deraining and desnowing in a multi-task learning setting. This…
Towards Full-to-Empty Room Generation with Structure-Aware Feature Encoding and Soft Semantic Region-Adaptive Normalization
Vasileios Gkitsas, Nikolaos Zioulis, Vladimiros Sterzentsenko +2
The task of transforming a furnished room image into a background-only is extremely challenging since it requires making large changes regarding the scene context while still prese…
Serverless Streaming for Emerging Media: Towards 5G Network-Driven Cost Optimization
Konstantinos Konstantoudakis, David Breitgand, Alexandros Doumanoglou +7
Immersive 3D media is an emerging type of media that captures, encodes and reconstructs the 3D appearance of people and objects, with applications in tele-presence, teleconference,…
Deep Soft Procrustes for Markerless Volumetric Sensor Alignment
Vladimiros Sterzentsenko, Alexandros Doumanoglou, Spyridon Thermos +3
With the advent of consumer grade depth sensors, low-cost volumetric capture systems are easier to deploy. Their wider adoption though depends on their usability and by extension o…
Self-Supervised Deep Depth Denoising
Vladimiros Sterzentsenko, Leonidas Saroglou, Anargyros Chatzitofis +5
Depth perception is considered an invaluable source of information for various vision tasks. However, depth maps acquired using consumer-level sensors still suffer from non-negligi…
A Low-Cost, Flexible and Portable Volumetric Capturing System
Vladimiros Sterzentsenko, Antonis Karakottas, Alexandros Papachristou +4
Multi-view capture systems are complex systems to engineer. They require technical knowledge to install and intricate processes to setup related mainly to the sensors' spatial alig…
Non-linear Convolution Filters for CNN-based Learning
Georgios Zoumpourlis, Alexandros Doumanoglou, Nicholas Vretos +1
During the last years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in image classification. Their architectures have largely drawn inspiration b…