Grigorios Tsagkatakis — accepted AI-conference papers

2026

THE MORE, THE MERRIER: CONTRASTIVE FUSION FOR HIGHER-ORDER MULTIMODAL ALIGNMENT

Stefanos Koutoupis, Michaela Areti Zervou, Konstantinos Kontras, Maarten De Vos, Panagiotis Tsakalides, Grigorios Tsagkatakis

CVPR 2026

Learning joint representations across multiple modalities remains a central challenge in multimodal machine learning. Prevailing approaches predominantly operate in pairwise settings, aligning two modalities at a time. While some recent methods aim to capture higher-order interactions among multiple

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2022

4D Convolutional Neural Networks for Multi-Spectral and Multi-Temporal Remote Sensing Data Classification

Michalis Giannopoulos, Grigorios Tsagkatakis, Panagiotis Tsakalides

ICASSP 2022accepted

Multi-temporal remotely sensed observations acquired by multi-spectral sensors contain a wealth of information related to the Earth’s state. Deep learning methods have demonstrated a great potential in analyzing such observations. Traditional 2D and 3D approaches are unable to effectively extract va…

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2020

Quantized Tensor Robust Principal Component Analysis

Anastasia Aidini, Grigorios Tsagkatakis, Panagiotis Tsakalides

ICASSP 2020accepted

High-dimensional data structures, known as tensors, are fundamental in many applications, including multispectral imaging and color video processing. Compression of such huge amount of multidimensional data collected over time is of paramount importance, necessitating the process of quantization of…

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2019

Alternating Direction Method of Multipliers for Semi-blind Astronomical Image Deconvolution

Konstantina Fotiadou, Grigorios Tsagkatakis, Panagiotis Tsakalides

ICASSP 2019accepted

High resolution astronomical imagery plays a critical role in multiple remote sensing applications. In this work, we introduce a novel post-acquisition computational technique aiming to recover the high-quality versions of blurry and degraded astronomical observations. Additionally, the proposed sch…

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2019

Efficient Convolutional Neural Network Weight Compression for Space Data Classification on Multi-fpga Platforms

George Pitsis, Grigorios Tsagkatakis, Christos Kozanitis, Ioannis Kalomoiris, Aggelos Ioannou, Apostolos Dollas +2

ICASSP 2019accepted

Convolutional Neural Networks (CNNs) represent the cutting edge in signal analysis tasks like classification and regression. Realization of such architectures in hardware capable of performing high throughput computations, with minimal energy consumption, is a key enabling factor towards the prolife…

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