Publications

Applying the maximum entropy principle to neural networks enhances multi-species distribution models

Ryckewaert, Marcos, Botella, Servajean, Bonnet, Joly
Methods in Ecology and Evolution

Biodiversity

RAMEN: Resolution-Adjustable Multimodal Encoder for Earth Observation

Houdré, Marcos, Riffaud de Turckheim, Ienco, et al.
CVPR 2026

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Earth observation

TimeSenCLIP: a time series vision-language model for remote sensing

Jain, Marcos, Ienco, Interdonato, et al.
ISPRS Journal of Photogrammetry and Remote Sensing

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Earth observation

Two-stage vision transformers and hard masking offer robust object representations

Aniraj, Dantas, Ienco, Marcos
ICPR 2026

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Interpretability

Clarity: the flexibility-interpretability trade-off in sparsity-aware concept bottleneck models

Panousis, Marcos
Transactions on Machine Learning Research

pdf

Interpretability

Atomizer: generalizing to new modalities by breaking satellite images down to a set of scalars

Riffaud de Turckheim, Lobry, Interdonato, Marcos
BMVC 2025 (Best Presentation Award)

pdf

Earth observation

Effective integration of drone technology for mapping and managing palm species in the Peruvian Amazon

Tagle Casapia, Cardenas, Marcos, Fernández, Bartholomeus, et al.
Nature Communications

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Earth observationBiodiversity

Fully automatic extraction of morphological traits from the web: utopia or reality?

Marcos, van de Vlasakker, Athanasiadis, Bonnet, Goëau, Joly, Kissling, Leblanc, van Proosdij
Applications in Plant Sciences

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Biodiversity

SenCLIP: enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting

Jain, Ienco, Interdonato, Berchoux, Marcos
WACV 2025

pdf

Earth observation

PDiscoFormer: relaxing part discovery constraints with vision transformers

Aniraj, Dantas, Ienco, Marcos
ECCV 2024

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Interpretability

Coarse-to-fine concept bottleneck models

Panousis, Ienco, Marcos
NeurIPS 2024

pdf

Interpretability

PDiscoNet: semantically consistent part discovery for fine-grained recognition

van der Klis, Alaniz, Mancini, Dantas, Ienco, Akata, Marcos
ICCV 2023

pdf

Interpretability

Masking strategies for background bias removal in computer vision models

Aniraj, Dantas, Ienco, Marcos
ICCV 2023 Workshops

pdf

Interpretability

Attribute prediction as multiple instance learning

Marcos, Potze, Xu, Tuia, Akata
Transactions on Machine Learning Research

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Interpretability

Learning decision trees recurrently through communication

Alaniz, Marcos, Schiele, Akata
CVPR 2021

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Interpretability

Social media and deep learning capture the aesthetic quality of the landscape

Havinga, Marcos, Bogaart, Hein, Tuia
Scientific Reports

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Earth observation

Spatial and temporal deep learning methods for deriving land-use following deforestation: a pan-tropical case study using Landsat time series

Masolele, De Sy, Herold, Marcos, Verbesselt, Gieseke, Mullissa, Martius
Remote Sensing of Environment

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Earth observation

RSVQA: visual question answering for remote sensing data

Lobry, Marcos, Murray, Tuia
IEEE Transactions on Geoscience and Remote Sensing

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Earth observation

Half a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning

Kellenberger, Marcos, Lobry, Tuia
IEEE Transactions on Geoscience and Remote Sensing

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Earth observationBiodiversity

Learning deep structured active contours end-to-end

Marcos, Tuia, Kellenberger, Zhang, Bai, Liao, Urtasun
CVPR 2018

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Earth observation

Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models

Marcos, Volpi, Kellenberger, Tuia
ISPRS Journal of Photogrammetry and Remote Sensing

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Earth observation

Rotation equivariant vector field networks

Marcos, Volpi, Komodakis, Tuia
ICCV 2017

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Earth observation

Learning rotation invariant convolutional filters for texture classification

Marcos, Volpi, Tuia
ICPR 2016

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Earth observation

Geospatial correspondences for multimodal registration

Marcos, Hamid, Tuia
CVPR 2016

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Earth observation