About me
I am a Ph.D. candidate at the Remote Sensing Laboratory at the National Technical University of Athens, working under the supervision of
Konstantinos Karantzalos
and
Nikos Komodakis.
My research focuses on self-supervised learning and explainable AI.
I have a Master’s degree in Mathematical Modeling from the National Technical University of Athens and a Bachelor’s degree in Mathematics from the University of Athens.
I serve as a reviewer at CVPR, ECCV, IJCV, IEEE TNNLS, Neural Networks, WACV, IEEE GRSL, IEEE Access.
I have a Master’s degree in Mathematical Modeling from the National Technical University of Athens and a Bachelor’s degree in Mathematics from the University of Athens.
I serve as a reviewer at CVPR, ECCV, IJCV, IEEE TNNLS, Neural Networks, WACV, IEEE GRSL, IEEE Access.
Papers
SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive Transformers
We designed an unsupervised object-centric learning framework that uses attention-based self-training and a novel patch-order permutation strategy for autoregressive transformers. This approach achieves state-of-the-art performance in unsupervised object segmentation, especially with complex real-world images.
Ioannis Kakogeorgiou, Spyros Gidaris, Konstantinos Karantzalos, Nikos Komodakis
CVPR, 2024 [CVPR Highlight (2.8% of submissions)]
paper | arXiv | code
We designed an unsupervised object-centric learning framework that uses attention-based self-training and a novel patch-order permutation strategy for autoregressive transformers. This approach achieves state-of-the-art performance in unsupervised object segmentation, especially with complex real-world images.
Ioannis Kakogeorgiou, Spyros Gidaris, Konstantinos Karantzalos, Nikos Komodakis
CVPR, 2024 [CVPR Highlight (2.8% of submissions)]
paper | arXiv | code
Composed Image Retrieval for Remote Sensing
We introduced composed image retrieval to remote sensing. It allows querying a large image archive using image examples alternated by a textual description. We presented a new evaluation benchmark for this task and proposed a novel method that fuses image-to-image and text-to-image similarity for effective composed image retrieval.
Bill Psomas, Ioannis Kakogeorgiou, Nikos Efthymiadis, Giorgos Tolias, Ondrej Chum, Yannis Avrithis, Konstantinos Karantzalos
IEEE IGARSS, 2024 [Oral]
paper | code
We introduced composed image retrieval to remote sensing. It allows querying a large image archive using image examples alternated by a textual description. We presented a new evaluation benchmark for this task and proposed a novel method that fuses image-to-image and text-to-image similarity for effective composed image retrieval.
Bill Psomas, Ioannis Kakogeorgiou, Nikos Efthymiadis, Giorgos Tolias, Ondrej Chum, Yannis Avrithis, Konstantinos Karantzalos
IEEE IGARSS, 2024 [Oral]
paper | code
A Comparative Study on Sentinel-2 Cloud Detection Algorithms in Marine Environments
We evaluated four well-established cloud detection algorithms (FMASK, SEN2COR, KAPPAMASK, and S2CLOUDLESS) in marine environments using the MARIDA dataset derived from Sentinel-2 satellite imagery. Our evaluation assessed performance across Cloud, Thin Cloud, Cloud Shadow, and Clear categories.
Ioannis Kakogeorgiou, Paraskevi Mikeli, Katerina Kikaki, Emmanouela Prassou, Konstantinos Karantzalos
IEEE IGARSS, 2024 [Oral]
paper
We evaluated four well-established cloud detection algorithms (FMASK, SEN2COR, KAPPAMASK, and S2CLOUDLESS) in marine environments using the MARIDA dataset derived from Sentinel-2 satellite imagery. Our evaluation assessed performance across Cloud, Thin Cloud, Cloud Shadow, and Clear categories.
Ioannis Kakogeorgiou, Paraskevi Mikeli, Katerina Kikaki, Emmanouela Prassou, Konstantinos Karantzalos
IEEE IGARSS, 2024 [Oral]
paper
Detecting Marine Pollutants and Sea Surface Features with Deep Learning in Sentinel-2 Imagery
We introduced a new open-access dataset named MADOS, which includes 15 classes, featuring oil spills and marine debris, based on Sentinel-2 multispectral satellite data. Moreover, we proposed a novel deep learning framework named MariNeXt, which outperforms all baselines.
Katerina Kikaki, Ioannis Kakogeorgiou, Ibrahim Hoteit, Konstantinos Karantzalos
ISPRS Journal of Photogrammetry and Remote Sensing, 2024
paper | code | project page
We introduced a new open-access dataset named MADOS, which includes 15 classes, featuring oil spills and marine debris, based on Sentinel-2 multispectral satellite data. Moreover, we proposed a novel deep learning framework named MariNeXt, which outperforms all baselines.
Katerina Kikaki, Ioannis Kakogeorgiou, Ibrahim Hoteit, Konstantinos Karantzalos
ISPRS Journal of Photogrammetry and Remote Sensing, 2024
paper | code | project page
Keep It SimPool: Who Said Supervised Transformers Suffer from Attention Deficit?
We developed a universal attention-based pooling mechanism called SimPool to replace default pooling strategies in both convolutional and transformer encoders, significantly improving performance and generating high-quality attention maps for both supervised and self-supervised settings.
Bill Psomas, Ioannis Kakogeorgiou, Konstantinos Karantzalos, Yannis Avrithis
ICCV, 2023
paper | arXiv | code
We developed a universal attention-based pooling mechanism called SimPool to replace default pooling strategies in both convolutional and transformer encoders, significantly improving performance and generating high-quality attention maps for both supervised and self-supervised settings.
Bill Psomas, Ioannis Kakogeorgiou, Konstantinos Karantzalos, Yannis Avrithis
ICCV, 2023
paper | arXiv | code
What to Hide from Your Students: Attention-Guided Masked Image Modeling
We introduce a novel masking strategy, called attention-guided masking (AttMask), and we demonstrate its effectiveness over random masking for dense distillation-based MIM.
Ioannis Kakogeorgiou, Spyros Gidaris, Bill Psomas, Yannis Avrithis, Andrei Bursuc, Konstantinos Karantzalos, Nikos Komodakis
ECCV, 2022
paper | DOI | arXiv | code
We introduce a novel masking strategy, called attention-guided masking (AttMask), and we demonstrate its effectiveness over random masking for dense distillation-based MIM.
Ioannis Kakogeorgiou, Spyros Gidaris, Bill Psomas, Yannis Avrithis, Andrei Bursuc, Konstantinos Karantzalos, Nikos Komodakis
ECCV, 2022
paper | DOI | arXiv | code
MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data
We present Marine Debris Archive (MARIDA), the first open-access dataset based on the multispectral Sentinel-2 (S2) satellite data, which distinguishes Marine Debris from various marine features that co-exist.
Katerina Kikaki, Ioannis Kakogeorgiou, Paraskevi Mikeli, Dionysios E. Raitsos, Konstantinos Karantzalos
PLOS One, 2022 [Sentinel Success Stories]
paper | code | project page
We present Marine Debris Archive (MARIDA), the first open-access dataset based on the multispectral Sentinel-2 (S2) satellite data, which distinguishes Marine Debris from various marine features that co-exist.
Katerina Kikaki, Ioannis Kakogeorgiou, Paraskevi Mikeli, Dionysios E. Raitsos, Konstantinos Karantzalos
PLOS One, 2022 [Sentinel Success Stories]
paper | code | project page
HOW CHALLENGING IS THE DISCRIMINATION OF FLOATING MATERIALS ON THE SEA SURFACE USING HIGH RESOLUTION MULTISPECTRAL SATELLITE DATA?
We explore the ability to discriminate marine debris from other floating materials and sea features using high-resolution multispectral satellite data. To perform our analysis, we utilized the open-access Marine Debris Archive (MARIDA). We indicate that the spectral information alone is insufficient to distinguish marine plastic from other floating materials which exhibit similar spectral behavior, such as vessels.
Paraskevi Mikeli, Katerina Kikaki, Ioannis Kakogeorgiou, Konstantinos Karantzalos
ISPRS Archives, 2022 [ISPRS Best Poster Award]
paper
We explore the ability to discriminate marine debris from other floating materials and sea features using high-resolution multispectral satellite data. To perform our analysis, we utilized the open-access Marine Debris Archive (MARIDA). We indicate that the spectral information alone is insufficient to distinguish marine plastic from other floating materials which exhibit similar spectral behavior, such as vessels.
Paraskevi Mikeli, Katerina Kikaki, Ioannis Kakogeorgiou, Konstantinos Karantzalos
ISPRS Archives, 2022 [ISPRS Best Poster Award]
paper
Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing
We evaluated quantitatively and qualitatively different aspects of ten XAI methods. We assess XAI methods’ performance for multi-label classification tasks in BigEarthNet and SEN12MS datasets employing various metrics. We extracted significant insights regarding models’ decisions as well as datasets’ composition and conclude that Occlusion, LIME and Grad-CAM were the most interpretable methods for the specific multi-label remote sensing classification task.
Ioannis Kakogeorgiou, Konstantinos Karantzalos
Int. J. Appl. Earth Obs. Geoinf., 2021
paper | arXiv
We evaluated quantitatively and qualitatively different aspects of ten XAI methods. We assess XAI methods’ performance for multi-label classification tasks in BigEarthNet and SEN12MS datasets employing various metrics. We extracted significant insights regarding models’ decisions as well as datasets’ composition and conclude that Occlusion, LIME and Grad-CAM were the most interpretable methods for the specific multi-label remote sensing classification task.
Ioannis Kakogeorgiou, Konstantinos Karantzalos
Int. J. Appl. Earth Obs. Geoinf., 2021
paper | arXiv