119 citations · 127 across the 18 of their papers we have counts for
19 papers
Testing the Test: Score-Direction Instability in Class-Split Anomaly Detection
Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz +2
Within-dataset class-split evaluation is widely used as a proxy for fully unconditional out-of-distribution anomaly detection. We show that this protocol can become ill-posed when…
High-Dimensional Latents Should Be Diagnosed Through Phase Structure
Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz +2
We study autoencoder and variational-autoencoder latent spaces through the lens of spin-glass theory. The paper has two components. First, we formalize a latent-space spin-glass di…
When Rule Violations Are Rare: Chimera Training for Logical Anomaly Detection
Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz +2
Many practical anomalies are not merely rare inputs, but violations of semantic constraints: objects co-occur in structured ways, actions imply preconditions, and events satisfy te…
NC-Reg : Neural Cortical Maps for Rigid Registration
Ines Vati, Pierrick Bourgeat, Rodrigo Santa Cruz +4
We introduce neural cortical maps, a continuous and compact neural representation for cortical feature maps, as an alternative to traditional discrete structures such as grids and…
Non-Invasive 3D Wound Measurement with RGB-D Imaging
Lena Harkämper, Leo Lebrat, David Ahmedt-Aristizabal +3
Chronic wound monitoring and management require accurate and efficient wound measurement methods. This paper presents a fast, non-invasive 3D wound measurement algorithm based on R…
Multi-View Consistent Wound Segmentation With Neural Fields
Remi Chierchia, Léo Lebrat, David Ahmedt-Aristizabal +4
Wound care is often challenged by the economic and logistical burdens that consistently afflict patients and hospitals worldwide. In recent decades, healthcare professionals have s…
VAE with Hyperspherical Coordinates: Improving Anomaly Detection from Hypervolume-Compressed Latent Space
Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz +2
Variational autoencoders (VAE) encode data into lower-dimensional latent vectors before decoding those vectors back to data. Once trained, one can hope to detect out-of-distributio…
SALVE: A 3D Reconstruction Benchmark of Wounds from Consumer-grade Videos
Remi Chierchia, Leo Lebrat, David Ahmedt-Aristizabal +3
Managing chronic wounds is a global challenge that can be alleviated by the adoption of automatic systems for clinical wound assessment from consumer-grade videos. While 2D image a…
NeRF Director: Revisiting View Selection in Neural Volume Rendering
Wenhui Xiao, Rodrigo Santa Cruz, David Ahmedt-Aristizabal +3
Neural Rendering representations have significantly contributed to the field of 3D computer vision. Given their potential, considerable efforts have been invested to improve their…
Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis
Léo Lebrat, Rodrigo Santa Cruz, Remi Chierchia +12
Wound management poses a significant challenge, particularly for bedridden patients and the elderly. Accurate diagnostic and healing monitoring can significantly benefit from moder…
CorticalFlow: Boosting Cortical Surface Reconstruction Accuracy, Regularity, and Interoperability
Rodrigo Santa Cruz, Léo Lebrat, Darren Fu +4
The problem of Cortical Surface Reconstruction from magnetic resonance imaging has been traditionally addressed using lengthy pipelines of image processing techniques like FreeSurf…
CorticalFlow: A Diffeomorphic Mesh Deformation Module for Cortical Surface Reconstruction
Léo Lebrat, Rodrigo Santa Cruz, Frédéric de Gournay +5
In this paper we introduce CorticalFlow, a new geometric deep-learning model that, given a 3-dimensional image, learns to deform a reference template towards a targeted object. To…
MongeNet: Efficient Sampler for Geometric Deep Learning
Léo Lebrat, Rodrigo Santa Cruz, Clinton Fookes +1
Recent advances in geometric deep-learning introduce complex computational challenges for evaluating the distance between meshes. From a mesh model, point clouds are necessary alon…
DeepCSR: A 3D Deep Learning Approach for Cortical Surface Reconstruction
Rodrigo Santa Cruz, Leo Lebrat, Pierrick Bourgeat +3
The study of neurodegenerative diseases relies on the reconstruction and analysis of the brain cortex from magnetic resonance imaging (MRI). Traditional frameworks for this task li…
Going deeper with brain morphometry using neural networks
Rodrigo Santa Cruz, Léo Lebrat, Pierrick Bourgeat +5
Brain morphometry from magnetic resonance imaging (MRI) is a consolidated biomarker for many neurodegenerative diseases. Recent advances in this domain indicate that deep convoluti…
Inferring Temporal Compositions of Actions Using Probabilistic Automata
Rodrigo Santa Cruz, Anoop Cherian, Basura Fernando +2
This paper presents a framework to recognize temporal compositions of atomic actions in videos. Specifically, we propose to express temporal compositions of actions as semantic reg…
Neural Algebra of Classifiers
Rodrigo Santa Cruz, Basura Fernando, Anoop Cherian +1
The world is fundamentally compositional, so it is natural to think of visual recognition as the recognition of basic visually primitives that are composed according to well-define…
DeepPermNet: Visual Permutation Learning
Rodrigo Santa Cruz, Basura Fernando, Anoop Cherian +1
We present a principled approach to uncover the structure of visual data by solving a novel deep learning task coined visual permutation learning. The goal of this task is to find…
On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization
Stephen Gould, Basura Fernando, Anoop Cherian +3
Some recent works in machine learning and computer vision involve the solution of a bi-level optimization problem. Here the solution of a parameterized lower-level problem binds va…