Search Results for author: Pablo Márquez-Neila

Found 15 papers, 8 papers with code

Hyperbolic Random Forests

1 code implementation25 Aug 2023 Lars Doorenbos, Pablo Márquez-Neila, Raphael Sznitman, Pascal Mettes

To make hyperbolic random forests work on multi-class data and imbalanced experiments, we furthermore outline a new method for combining classes based on their lowest common ancestor and a class-balanced version of the large-margin loss.

Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery

1 code implementation11 Apr 2023 Alain Jungo, Lars Doorenbos, Tommaso Da Col, Maarten Beelen, Martin Zinkernagel, Pablo Márquez-Neila, Raphael Sznitman

Detecting so-called out-of-distribution (OoD) samples is crucial in safety-critical applications such as robotically guided retinal microsurgery, where distances between the instrument and the retina are derived from sequences of 1D images that are acquired by an instrument-integrated optical coherence tomography (iiOCT) probe.

Out-of-Distribution Detection

Logical Implications for Visual Question Answering Consistency

1 code implementation CVPR 2023 Sergio Tascon-Morales, Pablo Márquez-Neila, Raphael Sznitman

Despite considerable recent progress in Visual Question Answering (VQA) models, inconsistent or contradictory answers continue to cast doubt on their true reasoning capabilities.

Language Modelling Question Answering +1

Stochastic Segmentation with Conditional Categorical Diffusion Models

1 code implementation ICCV 2023 Lukas Zbinden, Lars Doorenbos, Theodoros Pissas, Adrian Thomas Huber, Raphael Sznitman, Pablo Márquez-Neila

Semantic segmentation has made significant progress in recent years thanks to deep neural networks, but the common objective of generating a single segmentation output that accurately matches the image's content may not be suitable for safety-critical domains such as medical diagnostics and autonomous driving.

Autonomous Driving Denoising +2

ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei Detection

1 code implementation23 Aug 2022 Lars Doorenbos, Olena Torbaniuk, Stefano Cavuoti, Maurizio Paolillo, Giuseppe Longo, Massimo Brescia, Raphael Sznitman, Pablo Márquez-Neila

In this work, we focus on applying our method to the detection of AGN candidates in a Sloan Digital Sky Survey galaxy sample, since the identification and classification of Active Galactic Nuclei (AGN) in the optical band still remains a challenging task in extragalactic astronomy.

Astronomy Retrieval

Consistency-preserving Visual Question Answering in Medical Imaging

1 code implementation27 Jun 2022 Sergio Tascon-Morales, Pablo Márquez-Neila, Raphael Sznitman

Visual Question Answering (VQA) models take an image and a natural-language question as input and infer the answer to the question.

Question Answering Visual Question Answering

Data Invariants to Understand Unsupervised Out-of-Distribution Detection

no code implementations26 Nov 2021 Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila

Motivated by a simple thought experiment, we propose a characterization of U-OOD based on the invariants of the training dataset.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

Real-Time Camera Pose Estimation for Sports Fields

no code implementations31 Mar 2020 Leonardo Citraro, Pablo Márquez-Neila, Stefano Savarè, Vivek Jayaram, Charles Dubout, Félix Renaut, Andrés Hasfura, Horesh Ben Shitrit, Pascal Fua

Given an image sequence featuring a portion of a sports field filmed by a moving and uncalibrated camera, such as the one of the smartphones, our goal is to compute automatically in real time the focal length and extrinsic camera parameters for each image in the sequence without using a priori knowledges of the position and orientation of the camera.

Pose Estimation Position

Fused Detection of Retinal Biomarkers in OCT Volumes

no code implementations16 Jul 2019 Thomas Kurmann, Pablo Márquez-Neila, Siqing Yu, Marion Munk, Sebastian Wolf, Raphael Sznitman

In this context, we present a method that automatically predicts the presence of biomarkers in OCT cross-sections by incorporating information from the entire volume.

Simulation of hyperelastic materials in real-time using Deep Learning

no code implementations10 Apr 2019 Andrea Mendizabal, Pablo Márquez-Neila, Stéphane Cotin

In this paper we present U-Mesh: a data-driven method based on a U-Net architecture that approximates the non-linear relation between a contact force and the displacement field computed by a FEM algorithm.

Cantilever Beam

Self-Binarizing Networks

no code implementations2 Feb 2019 Fayez Lahoud, Radhakrishna Achanta, Pablo Márquez-Neila, Sabine Süsstrunk

To obtain similar binary networks, existing methods rely on the sign activation function.

Binarization

Imposing Hard Constraints on Deep Networks: Promises and Limitations

no code implementations7 Jun 2017 Pablo Márquez-Neila, Mathieu Salzmann, Pascal Fua

Imposing constraints on the output of a Deep Neural Net is one way to improve the quality of its predictions while loosening the requirements for labeled training data.

Uniform Information Segmentation

no code implementations27 Nov 2016 Radhakrishna Achanta, Pablo Márquez-Neila, Pascal Fua, Sabine Süsstrunk

Since information is a natural way of measuring image complexity, our proposed algorithm leads to image segments that are smaller and denser in areas of high complexity and larger in homogeneous regions, thus simplifying the image while preserving its details.

Segmentation Superpixels

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