232 citations · 760 across the 30 of their papers we have counts for
33 papers
Unleashing Uncertainty: Efficient Machine Unlearning for Generative AI
Christoforos N. Spartalis, Theodoros Semertzidis, Petros Daras +1
We introduce SAFEMax, a novel method for Machine Unlearning in diffusion models. Grounded in information-theoretic principles, SAFEMax maximizes the entropy in generated images, ca…
LoTUS: Large-Scale Machine Unlearning with a Taste of Uncertainty
Christoforos N. Spartalis, Theodoros Semertzidis, Petros Daras +1
We present LoTUS, a novel Machine Unlearning (MU) method that eliminates the influence of training samples from pre-trained models, avoiding retraining from scratch. LoTUS smooths…
MC-hands-1M: A glove-wearing hand dataset for pose estimation
Prodromos Boutis, Zisis Batzos, Konstantinos Konstantoudakis +2
Nowadays, the need for large amounts of carefully and complexly annotated data for the training of computer vision modules continues to grow. Furthermore, although the research com…
Hybrid Skip: A Biologically Inspired Skip Connection for the UNet Architecture
Nikolaos Zioulis, Georgios Albanis, Petros Drakoulis +3
In this work we introduce a biologically inspired long-range skip connection for the UNet architecture that relies on the perceptual illusion of hybrid images, being images that si…
Multi-manifold Attention for Vision Transformers
Dimitrios Konstantinidis, Ilias Papastratis, Kosmas Dimitropoulos +1
Vision Transformers are very popular nowadays due to their state-of-the-art performance in several computer vision tasks, such as image classification and action recognition. Altho…
Monocular Spherical Depth Estimation with Explicitly Connected Weak Layout Cues
Nikolaos Zioulis, Federico Alvarez, Dimitrios Zarpalas +1
Spherical cameras capture scenes in a holistic manner and have been used for room layout estimation. Recently, with the availability of appropriate datasets, there has also been pr…
SHREC 2022: Protein-ligand binding site recognition
Luca Gagliardi, Andrea Raffo, Ulderico Fugacci +12
This paper presents the methods that have participated in the SHREC 2022 contest on protein-ligand binding site recognition. The prediction of protein-ligand binding regions is an…
A benchmark with decomposed distribution shifts for 360 monocular depth estimation
Georgios Albanis, Nikolaos Zioulis, Petros Drakoulis +3
In this work we contribute a distribution shift benchmark for a computer vision task; monocular depth estimation. Our differentiation is the decomposition of the wider distribution…
On Coordinate Decoding for Keypoint Estimation Tasks
Anargyros Chatzitofis, Nikolaos Zioulis, Georgios Nikolaos Albanis +2
A series of 2D (and 3D) keypoint estimation tasks are built upon heatmap coordinate representation, i.e. a probability map that allows for learnable and spatially aware encoding an…
HUMAN4D: A Human-Centric Multimodal Dataset for Motions and Immersive Media
Anargyros Chatzitofis, Leonidas Saroglou, Prodromos Boutis +9
We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric captu…
DeepMoCap: Deep Optical Motion Capture Using Multiple Depth Sensors and Retro-Reflectors
Anargyros Chatzitofis, Dimitrios Zarpalas, Stefanos Kollias +1
In this paper, a marker-based, single-person optical motion capture method (DeepMoCap) is proposed using multiple spatio-temporally aligned infrared-depth sensors and retro-reflect…
Pano3D: A Holistic Benchmark and a Solid Baseline for Depth Estimation
Georgios Albanis, Nikolaos Zioulis, Petros Drakoulis +5
Pano3D is a new benchmark for depth estimation from spherical panoramas. It aims to assess performance across all depth estimation traits, the primary direct depth estimation perfo…
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,…
Single-Shot Cuboids: Geodesics-based End-to-end Manhattan Aligned Layout Estimation from Spherical Panoramas
Nikolaos Zioulis, Federico Alvarez, Dimitrios Zarpalas +1
It has been shown that global scene understanding tasks like layout estimation can benefit from wider field of views, and specifically spherical panoramas. While much progress has…
SHREC 2020 track: 6D Object Pose Estimation
Honglin Yuan, Remco C. Veltkamp, Georgios Albanis +3
6D pose estimation is crucial for augmented reality, virtual reality, robotic manipulation and visual navigation. However, the problem is challenging due to the variety of objects…
Comprehensive Comparison of Deep Learning Models for Lung and COVID-19 Lesion Segmentation in CT scans
Paschalis Bizopoulos, Nicholas Vretos, Petros Daras
Recently there has been an explosion in the use of Deep Learning (DL) methods for medical image segmentation. However the field's reliability is hindered by the lack of a common ba…
Multi-view adaptive graph convolutions for graph classification
Nikolas Adaloglou, Nicholas Vretos, Petros Daras
In this paper, a novel multi-view methodology for graph-based neural networks is proposed. A systematic and methodological adaptation of the key concepts of classical deep learning…
A Comprehensive Study on Deep Learning-based Methods for Sign Language Recognition
Nikolas Adaloglou, Theocharis Chatzis, Ilias Papastratis +7
In this paper, a comparative experimental assessment of computer vision-based methods for sign language recognition is conducted. By implementing the most recent deep neural networ…
Deep Lighting Environment Map Estimation from Spherical Panoramas
Vasileios Gkitsas, Nikolaos Zioulis, Federico Alvarez +2
Estimating a scene's lighting is a very important task when compositing synthetic content within real environments, with applications in mixed reality and post-production. In this…
A Deep Learning Approach to Object Affordance Segmentation
Spyridon Thermos, Petros Daras, Gerasimos Potamianos
Learning to understand and infer object functionalities is an important step towards robust visual intelligence. Significant research efforts have recently focused on segmenting th…
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…
DeepSurf: A surface-based deep learning approach for the prediction of ligand binding sites on proteins
Stelios K. Mylonas, Apostolos Axenopoulos, Petros Daras
The knowledge of potentially druggable binding sites on proteins is an important preliminary step towards the discovery of novel drugs. The computational prediction of such areas c…
An Improved Tobit Kalman Filter with Adaptive Censoring Limits
Kostas Loumponias, Nicholas Vretos, George Tsaklidis +1
This paper deals with the Tobit Kalman filtering (TKF) process when the measurements are correlated and censored. The case of interval censoring, i.e., the case of measurements whi…
Restyling Data: Application to Unsupervised Domain Adaptation
Vasileios Gkitsas, Antonis Karakottas, Nikolaos Zioulis +2
Machine learning is driven by data, yet while their availability is constantly increasing, training data require laborious, time consuming and error-prone labelling or ground truth…