1.7k citations · 2.7k across the 20 of their papers we have counts for
24 papers
Learning to Name Classes for Vision and Language Models
Sarah Parisot, Yongxin Yang, Steven McDonagh
Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges tha…
Content-Diverse Comparisons improve IQA
William Thong, Jose Costa Pereira, Sarah Parisot +2
Image quality assessment (IQA) forms a natural and often straightforward undertaking for humans, yet effective automation of the task remains highly challenging. Recent metrics fro…
CLAD: A realistic Continual Learning benchmark for Autonomous Driving
Eli Verwimp, Kuo Yang, Sarah Parisot +5
In this paper we describe the design and the ideas motivating a new Continual Learning benchmark for Autonomous Driving (CLAD), that focuses on the problems of object classificatio…
Re-examining Distillation For Continual Object Detection
Eli Verwimp, Kuo Yang, Sarah Parisot +5
Training models continually to detect and classify objects, from new classes and new domains, remains an open problem. In this work, we conduct a thorough analysis of why and how o…
Long-tail Recognition via Compositional Knowledge Transfer
Sarah Parisot, Pedro M. Esperanca, Steven McDonagh +3
In this work, we introduce a novel strategy for long-tail recognition that addresses the tail classes' few-shot problem via training-free knowledge transfer. Our objective is to tr…
Learning Compositional Shape Priors for Few-Shot 3D Reconstruction
Mateusz Michalkiewicz, Stavros Tsogkas, Sarah Parisot +3
The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of…
Probabilistic 3D surface reconstruction from sparse MRI information
Katarína Tóthová, Sarah Parisot, Matthew Lee +4
Surface reconstruction from magnetic resonance (MR) imaging data is indispensable in medical image analysis and clinical research. A reliable and effective reconstruction tool shou…
Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection
Carlo Biffi, Steven McDonagh, Philip Torr +2
Object detection has witnessed significant progress by relying on large, manually annotated datasets. Annotating such datasets is highly time consuming and expensive, which motivat…
Low Light Video Enhancement using Synthetic Data Produced with an Intermediate Domain Mapping
Danai Triantafyllidou, Sean Moran, Steven McDonagh +2
Advances in low-light video RAW-to-RGB translation are opening up the possibility of fast low-light imaging on commodity devices (e.g. smartphone cameras) without the need for a tr…
Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors
Mateusz Michalkiewicz, Sarah Parisot, Stavros Tsogkas +3
The impressive performance of deep convolutional neural networks in single-view 3D reconstruction suggests that these models perform non-trivial reasoning about the 3D structure of…
DeepLPF: Deep Local Parametric Filters for Image Enhancement
Sean Moran, Pierre Marza, Steven McDonagh +2
Digital artists often improve the aesthetic quality of digital photographs through manual retouching. Beyond global adjustments, professional image editing programs provide local a…
Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem
Matthias De Lange, Xu Jia, Sarah Parisot +3
This work investigates the task of unsupervised model personalization, adapted to continually evolving, unlabeled local user images. We consider the practical scenario where a high…
A Multi-Hypothesis Approach to Color Constancy
Daniel Hernandez-Juarez, Sarah Parisot, Benjamin Busam +3
Contemporary approaches frame the color constancy problem as learning camera specific illuminant mappings. While high accuracy can be achieved on camera specific data, these models…
EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement
Linpu Fang, Hang Xu, Zhili Liu +2
Object detectors trained on fully-annotated data currently yield state of the art performance but require expensive manual annotations. On the other hand, weakly-supervised detecto…
A continual learning survey: Defying forgetting in classification tasks
Matthias De Lange, Rahaf Aljundi, Marc Masana +5
Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct trai…
Formulating Camera-Adaptive Color Constancy as a Few-shot Meta-Learning Problem
Steven McDonagh, Sarah Parisot, Fengwei Zhou +4
Digital camera pipelines employ color constancy methods to estimate an unknown scene illuminant, in order to re-illuminate images as if they were acquired under an achromatic light…
Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors
Katarína Tóthová, Sarah Parisot, Matthew C. H. Lee +5
Surface reconstruction is a vital tool in a wide range of areas of medical image analysis and clinical research. Despite the fact that many methods have proposed solutions to the r…
Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease
Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante +4
Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical applications, specifically, nodes c…
Learning Conditioned Graph Structures for Interpretable Visual Question Answering
Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot
Visual Question answering is a challenging problem requiring a combination of concepts from Computer Vision and Natural Language Processing. Most existing approaches use a two stre…
Exploring Heritability of Functional Brain Networks with Inexact Graph Matching
Sofia Ira Ktena, Salim Arslan, Sarah Parisot +1
Data-driven brain parcellations aim to provide a more accurate representation of an individual's functional connectivity, since they are able to capture individual variability that…
Spectral Graph Convolutions for Population-based Disease Prediction
Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante +4
Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as…
Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks
Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante +4
Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects…
Proceedings of the Workshop on Brain Analysis using COnnectivity Networks - BACON 2016
Sarah Parisot, Jonathan Passerat-Palmbach, Markus D. Schirmer +1
Understanding brain connectivity in a network-theoretic context has shown much promise in recent years. This type of analysis identifies brain organisational principles, bringing a…
Comparison of Brain Networks with Unknown Correspondences
Sofia Ira Ktena, Sarah Parisot, Jonathan Passerat-Palmbach +1
Graph theory has drawn a lot of attention in the field of Neuroscience during the last decade, mainly due to the abundance of tools that it provides to explore the interactions of…