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09/06/2023
PDiscoNet: Semantically consistent part discovery for fine-grained recognition
Fine-grained classification often requires recognizing specific object p...
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09/01/2023
Time Series Analysis of Urban Liveability
In this paper we explore deep learning models to monitor longitudinal li...
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08/23/2023
Masking Strategies for Background Bias Removal in Computer Vision Models
Models for fine-grained image classification tasks, where the difference...
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08/21/2023
Sparse Linear Concept Discovery Models
The recent mass adoption of DNNs, even in safety-critical scenarios, has...
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08/07/2023
The GeoLifeCLEF 2023 Dataset to evaluate plant species distribution models at high spatial resolution across Europe
The difficulty to measure or predict species community composition at fi...
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01/04/2023
Counterfactual Explanations for Land Cover Mapping in a Multi-class Setting
Counterfactual explanations are an emerging tool to enhance interpretabi...
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07/27/2022
Abstracting Sketches through Simple Primitives
Humans show high-level of abstraction capabilities in games that require...
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05/18/2022
A weakly supervised framework for high-resolution crop yield forecasts
Predictor inputs and label data for crop yield forecasting are not alway...
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01/20/2021
Self-supervised pre-training enhances change detection in Sentinel-2 imagery
While annotated images for change detection using satellite imagery are ...
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01/10/2021
Semantic Segmentation of Remote Sensing Images with Sparse Annotations
Training Convolutional Neural Networks (CNNs) for very high resolution i...
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12/07/2020
Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization
Remote sensing image classification exploiting multiple sensors is a ver...
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09/18/2020
Contextual Semantic Interpretability
Convolutional neural networks (CNN) are known to learn an image represen...
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03/16/2020
RSVQA: Visual Question Answering for Remote Sensing Data
This paper introduces the task of visual question answering for remote s...
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09/18/2019
Semantically Interpretable Activation Maps: what-where-how explanations within CNNs
A main issue preventing the use of Convolutional Neural Networks (CNN) i...
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07/17/2019
Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning
We present an Active Learning (AL) strategy for re-using a deep Convolut...
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07/31/2018
Scale equivariance in CNNs with vector fields
We study the effect of injecting local scale equivariance into Convoluti...
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06/29/2018
Detecting Mammals in UAV Images: Best Practices to address a substantially Imbalanced Dataset with Deep Learning
Knowledge over the number of animals in large wildlife reserves is a vit...
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03/16/2018
Learning deep structured active contours end-to-end
The world is covered with millions of buildings, and precisely knowing e...
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03/16/2018
Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models
In remote sensing images, the absolute orientation of objects is arbitra...
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12/29/2016
Rotation equivariant vector field networks
In many computer vision tasks, we expect a particular behavior of the ou...
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04/22/2016