1 code implementation • 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2015 • Konstantinos Makantasis, Konstantinos Karantzalos, Anastasios Doulamis, Nikolaos Doulamis
Our method exploits a Convolutional Neural Network to encode pixels' spectral and spatial information and a Multi-Layer Perceptron to conduct the classification task.
no code implementations • 27 Feb 2019 • Panagiotis Agrafiotis, Dimitrios Skarlatos, Andreas Georgopoulos, Konstantinos Karantzalos
The determination of accurate bathymetric information is a key element for near offshore activities, hydrological studies such as coastal engineering applications, sedimentary processes, hydrographic surveying as well as archaeological mapping and biological research.
4 code implementations • 17 Oct 2019 • Maria Papadomanolaki, Sagar Verma, Maria Vakalopoulou, Siddharth Gupta, Konstantinos Karantzalos
\begin{abstract} The advent of multitemporal high resolution data, like the Copernicus Sentinel-2, has enhanced significantly the potential of monitoring the earth's surface and environmental dynamics.
no code implementations • 3 Apr 2021 • Ioannis Kakogeorgiou, Konstantinos Karantzalos
Although deep neural networks hold the state-of-the-art in several remote sensing tasks, their black-box operation hinders the understanding of their decisions, concealing any bias and other shortcomings in datasets and model performance.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1
1 code implementation • ICLR 2022 • Shashanka Venkataramanan, Bill Psomas, Ewa Kijak, Laurent Amsaleg, Konstantinos Karantzalos, Yannis Avrithis
In this work, we aim to bridge this gap and improve representations using mixup, which is a powerful data augmentation approach interpolating two or more examples and corresponding target labels at a time.
Ranked #8 on Metric Learning on CUB-200-2011 (using extra training data)
1 code implementation • Plos one journal 2022 • Katerina Kikaki, Ioannis Kakogeorgiou, Paraskevi Mikeli, Dionysios E. Raitsos, Konstantinos Karantzalos
Currently, a significant amount of research is focused on detecting Marine Debris and assessing its spectral behaviour via remote sensing, ultimately aiming at new operational monitoring solutions.
Ranked #2 on Image Segmentation on MARIDA
1 code implementation • 23 Mar 2022 • Ioannis Kakogeorgiou, Spyros Gidaris, Bill Psomas, Yannis Avrithis, Andrei Bursuc, Konstantinos Karantzalos, Nikos Komodakis
In this work, we argue that image token masking differs from token masking in text, due to the amount and correlation of tokens in an image.
1 code implementation • 6 Aug 2022 • Athena Psalta, Vasileios Tsironis, Konstantinos Karantzalos
Data association is a crucial component for any multiple object tracking (MOT) method that follows the tracking-by-detection paradigm.
no code implementations • 21 Aug 2022 • Aikaterini Adam, Torsten Sattler, Konstantinos Karantzalos, Tomas Pajdla
AR/VR applications and robots need to know when the scene has changed.
no code implementations • 20 Dec 2022 • Panagiotis Agrafiotis, Konstantinos Karantzalos, Andreas Georgopoulos
Mapping the seafloor with underwater imaging cameras is of significant importance for various applications including marine engineering, geology, geomorphology, archaeology and biology.
1 code implementation • ICCV 2023 • Bill Psomas, Ioannis Kakogeorgiou, Konstantinos Karantzalos, Yannis Avrithis
By discussing the properties of each group of methods, we derive SimPool, a simple attention-based pooling mechanism as a replacement of the default one for both convolutional and transformer encoders.
1 code implementation • 6 Nov 2023 • Maria Sdraka, Alkinoos Dimakos, Alexandros Malounis, Zisoula Ntasiou, Konstantinos Karantzalos, Dimitrios Michail, Ioannis Papoutsis
We use FLOGA to provide a thorough comparison of multiple Machine Learning and Deep Learning algorithms for the automatic extraction of burnt areas, approached as a change detection task.
1 code implementation • 1 Dec 2023 • Ioannis Kakogeorgiou, Spyros Gidaris, Konstantinos Karantzalos, Nikos Komodakis
Unsupervised object-centric learning aims to decompose scenes into interpretable object entities, termed slots.
no code implementations • 2 Dec 2023 • Aikaterini Adam, Konstantinos Karantzalos, Lazaros Grammatikopoulos, Torsten Sattler
Through scene comparison over time, information about objects in the scene and their changes is inferred.