1 citations · 2 across the 20 of their papers we have counts for
25 papers
Learning to See through Action: Predictive Curiosity for Robots
Stelios Zarifis, Panagiotis Filntisis, George Retsinas +1
Humans and animals do not learn vision from a fixed set of data: they move, interact, and learn from the visual consequences of their actions. Motivated by this developmental view,…
GLAM-SLAM: Real-time Gaussian Large-scale Mapping via Flow Densification and Spatial Decomposition
Panagiotis Mermigkas, Argyris Manetas, Petros Maragos
Existing Gaussian-splatting-based monocular Simultaneous Localization and Mapping (SLAM) systems are either tailored to short sequences, are not real-time, or suffer from prohibiti…
Uncertainty-Driven Anomaly Detection for Psychotic Relapse Using Smartwatches: Forecasting and Multi-Task Learning Fusion
Nikolaos Tsalkitzis, Panagiotis P. Filntisis, Petros Maragos +1
Digital phenotyping enables continuous passive monitoring of behavior and physiology, offering a promising paradigm for early detection of psychotic relapse. In this work, we devel…
Registration-Free Learnable Multi-View Capture of Faces in Dense Semantic Correspondence
Panagiotis P. Filntisis, George Retsinas, Radek Daněček +3
Recent frameworks like ToFu and TEMPEH provide an automated alternative to classical registration pipelines by predicting 3D meshes in dense semantic correspondence directly from c…
NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results
Andrey Moskalenko, Alexey Bryncev, Ivan Kosmynin +40
This paper presents an overview of the NTIRE 2026 Challenge on Video Saliency Prediction. The goal of the challenge participants was to develop automatic saliency map prediction me…
VGGT-HPE: Reframing Head Pose Estimation as Relative Pose Prediction
Vasiliki Vasileiou, Panagiotis P. Filntisis, Petros Maragos +1
Monocular head pose estimation is traditionally formulated as direct regression from a single image to an absolute pose. This paradigm forces the network to implicitly internalize…
Mind the Shape Gap: A Benchmark and Baseline for Deformation-Aware 6D Pose Estimation of Agricultural Produce
Nikolas Chatzis, Angeliki Tsinouka, Katerina Papadimitriou +7
Accurate 6D pose estimation for robotic harvesting is fundamentally hindered by the biological deformability and high intra-class shape variability of agricultural produce. Instanc…
Age-Inclusive 3D Human Mesh Recovery for Action-Preserving Data Anonymization
Georgios Chatzichristodoulou, Niki Efthymiou, Panagiotis Filntisis +2
While three-dimensional (3D) shape and pose estimation is a highly researched area that has yielded significant advances, the resulting methods, despite performing well for the adu…
Baby Sophia: A Developmental Approach to Self-Exploration through Self-Touch and Hand Regard
Stelios Zarifis, Ioannis Chalkiadakis, Artemis Chardouveli +7
Inspired by infant development, we propose a Reinforcement Learning (RL) framework for autonomous self-exploration in a robotic agent, Baby Sophia, using the BabyBench simulation e…
Teager-Kaiser Energy Methods For EEG Feature Extraction In Biomedical Applications
Ioanna Chourdaki, Kleanthis Avramidis, Christos Garoufis +2
Electroencephalography (EEG) signals are inherently non-linear, non-stationary, and vulnerable to noise sources, making the extraction of discriminative features a long-standing ch…
CVSM: Contrastive Vocal Similarity Modeling
Christos Garoufis, Athanasia Zlatintsi, Petros Maragos
The availability of large, unlabeled datasets across various domains has contributed to the development of a plethora of methods that learn representations for multiple target (dow…
Instance-Level Composed Image Retrieval
Bill Psomas, George Retsinas, Nikos Efthymiadis +5
The progress of composed image retrieval (CIR), a popular research direction in image retrieval, where a combined visual and textual query is used, is held back by the absence of h…
Shared-Weights Extender and Gradient Voting for Neural Network Expansion
Nikolas Chatzis, Ioannis Kordonis, Manos Theodosis +1
Expanding neural networks during training is a promising way to augment capacity without retraining larger models from scratch. However, newly added neurons often fail to adjust to…
Optimal Transport for Handwritten Text Recognition in a Low-Resource Regime
Petros Georgoulas Wraight, Giorgos Sfikas, Ioannis Kordonis +2
Handwritten Text Recognition (HTR) is a task of central importance in the field of document image understanding. State-of-the-art methods for HTR require the use of extensive annot…
Classical Guitar Duet Separation using GuitarDuets -- a Dataset of Real and Synthesized Guitar Recordings
Marios Glytsos, Christos Garoufis, Athanasia Zlatintsi +1
Recent advancements in music source separation (MSS) have focused in the multi-timbral case, with existing architectures tailored for the separation of distinct instruments, overlo…
Only-Style: Stylistic Consistency in Image Generation without Content Leakage
Tilemachos Aravanis, Panagiotis Filntisis, Petros Maragos +1
Generating images in a consistent reference visual style remains a challenging computer vision task. State-of-the-art methods aiming for style-consistent generation struggle to eff…
Category-Level 6D Object Pose Estimation in Agricultural Settings Using a Lattice-Deformation Framework and Diffusion-Augmented Synthetic Data
Marios Glytsos, Panagiotis P. Filntisis, George Retsinas +1
Accurate 6D object pose estimation is essential for robotic grasping and manipulation, particularly in agriculture, where fruits and vegetables exhibit high intra-class variability…
Object-Centric Action-Enhanced Representations for Robot Visuo-Motor Policy Learning
Nikos Giannakakis, Argyris Manetas, Panagiotis P. Filntisis +2
Learning visual representations from observing actions to benefit robot visuo-motor policy generation is a promising direction that closely resembles human cognitive function and p…
Proactive tactile exploration for object-agnostic shape reconstruction from minimal visual priors
Paris Oikonomou, George Retsinas, Petros Maragos +1
The perception of an object's surface is important for robotic applications enabling robust object manipulation. The level of accuracy in such a representation affects the outcome…
Training Deep Morphological Neural Networks as Universal Approximators
Konstantinos Fotopoulos, Petros Maragos
We investigate deep morphological neural networks (DMNNs), studying how changes in algebraic structure affect the expressivity and trainability of deep architectures. We show that…
Sparse Hybrid Linear-Morphological Networks
Konstantinos Fotopoulos, Christos Garoufis, Petros Maragos
We investigate hybrid linear-morphological networks. Recent studies highlight the inherent affinity of morphological layers to pruning, but also their difficulty in training. We pr…
A Transformer-Based Framework for Greek Sign Language Production using Extended Skeletal Motion Representations
Chrysa Pratikaki, Panagiotis Filntisis, Athanasios Katsamanis +2
Sign Languages are the primary form of communication for Deaf communities across the world. To break the communication barriers between the Deaf and Hard-of-Hearing and the hearing…
Pre-training for Action Recognition with Automatically Generated Fractal Datasets
Davyd Svyezhentsev, George Retsinas, Petros Maragos
In recent years, interest in synthetic data has grown, particularly in the context of pre-training the image modality to support a range of computer vision tasks, including object…
TropNNC: Structured Neural Network Compression Using Tropical Geometry
Konstantinos Fotopoulos, Petros Maragos, Panagiotis Misiakos
We present TropNNC, a framework for compressing neural networks with linear and convolutional layers and ReLU-type activations using tropical geometry. By representing a network's…