Search Results for author:
Found 26 papers, 11 papers with code
Contrastive Pretraining for Visual Concept Explanations of Socioeconomic Outcomes
This improves the model's interpretability as it enables the latent space of the model to associate urban concepts with continuous intervals of socioeconomic outcomes.
Cross-Modal Learning of Housing Quality in Amsterdam
In our research we test data and models for the recognition of housing quality in the city of Amsterdam from ground-level and aerial imagery.
Hierarchical Concept Discovery Models: A Concept Pyramid Scheme
Deep Learning algorithms have recently gained significant attention due to their impressive performance.
PDiscoNet: Semantically consistent part discovery for fine-grained recognition
Fine-grained classification often requires recognizing specific object parts, such as beak shape and wing patterns for birds.
Time Series Analysis of Urban Liveability
In this paper we explore deep learning models to monitor longitudinal liveability changes in Dutch cities at the neighbourhood level.
Masking Strategies for Background Bias Removal in Computer Vision Models
Models for fine-grained image classification tasks, where the difference between some classes can be extremely subtle and the number of samples per class tends to be low, are particularly prone to picking up background-related biases and demand robust methods to handle potential examples with out-of-distribution (OOD) backgrounds.
Sparse Linear Concept Discovery Models
The recent mass adoption of DNNs, even in safety-critical scenarios, has shifted the focus of the research community towards the creation of inherently intrepretable models.
The GeoLifeCLEF 2023 Dataset to evaluate plant species distribution models at high spatial resolution across Europe
We designed a European scale dataset covering around ten thousand plant species to calibrate and evaluate SDM predictions of species composition in space and time at high spatial resolution (~ten meters), and their spatial transferability.
Towards Explainable Land Cover Mapping: a Counterfactual-based Strategy
Furthermore, plausibility/realism of the generated counterfactual explanations is enforced via the proposed adversarial learning strategy.
Abstracting Sketches through Simple Primitives
Toward equipping machines with such capabilities, we propose the Primitive-based Sketch Abstraction task where the goal is to represent sketches using a fixed set of drawing primitives under the influence of a budget.
A weakly supervised framework for high-resolution crop yield forecasts
We propose a deep learning framework that uses high resolution inputs and low resolution labels to produce crop yield forecasts for both spatial levels.
Self-supervised pre-training enhances change detection in Sentinel-2 imagery
While annotated images for change detection using satellite imagery are scarce and costly to obtain, there is a wealth of unlabeled images being generated every day.
Ranked #5 on
Change Detection
on OSCD - 13ch
(using extra training data)
Semantic Segmentation of Remote Sensing Images with Sparse Annotations
Training Convolutional Neural Networks (CNNs) for very high resolution images requires a large quantity of high-quality pixel-level annotations, which is extremely labor- and time-consuming to produce.
Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization
Remote sensing image classification exploiting multiple sensors is a very challenging problem: data from different modalities are affected by spectral distortions and mis-alignments of all kinds, and this hampers re-using models built for one image to be used successfully in other scenes.
Contextual Semantic Interpretability
Once the attributes are learned, they can be re-combined to reach the final decision and provide both an accurate prediction and an explicit reasoning behind the CNN decision.
RSVQA: Visual Question Answering for Remote Sensing Data
We report the results obtained by applying a model based on Convolutional Neural Networks (CNNs) for the visual part and on a Recurrent Neural Network (RNN) for the natural language part to this task.
Semantically Interpretable Activation Maps: what-where-how explanations within CNNs
This gives the user insight into what the model has seen, where, and a final output directly linked to this information in a comprehensive and interpretable way.
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 Convolutional Neural Network (CNN)-based object detector on a new dataset.
Learning Decision Trees Recurrently Through Communication
Integrated interpretability without sacrificing the prediction accuracy of decision making algorithms has the potential of greatly improving their value to the user.
Scale equivariance in CNNs with vector fields
We study the effect of injecting local scale equivariance into Convolutional Neural Networks.
Detecting Mammals in UAV Images: Best Practices to address a substantially Imbalanced Dataset with Deep Learning
In this paper, we study how to scale CNNs to large wildlife census tasks and present a number of recommendations to train a CNN on a large UAV dataset.
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 arbitrary.
Learning deep structured active contours end-to-end
The world is covered with millions of buildings, and precisely knowing each instance's position and extents is vital to a multitude of applications.
Rotation equivariant vector field networks
In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image.
Ranked #8 on
Multi-tissue Nucleus Segmentation
on Kumar
Breast Tumour Classification
Colorectal Gland Segmentation:
+5
Geospatial Correspondences for Multimodal Registration
The growing availability of very high resolution (<1 m/pixel) satellite and aerial images has opened up unprecedented opportunities to monitor and analyze the evolution of land-cover and land-use across the world.
Learning rotation invariant convolutional filters for texture classification
We present a method for learning discriminative filters using a shallow Convolutional Neural Network (CNN).
