1 code implementation • ECCV 2020 • Yu Liu, Sarah Parisot, Gregory Slabaugh, Xu Jia, Ales Leonardis, Tinne Tuytelaars
Since those regularization strategies are mostly associated with classifier outputs, we propose a MUlti-Classifier (MUC) incremental learning paradigm that integrates an ensemble of auxiliary classifiers to estimate more effective regularization constraints.
no code implementations • 16 Nov 2024 • Alessandro Fontanella, Petru-Daniel Tudosiu, Yongxin Yang, Shifeng Zhang, Sarah Parisot
To ensure control over instance attributes, we devise a novel training paradigm to adapt a diffusion model to generate isolated scene components as RGBA images with transparency information.
no code implementations • 7 Oct 2024 • Danai Triantafyllidou, Sarah Parisot, Ales Leonardis, Steven McDonagh
However, existing methods often fail to handle content-rich scenes with multiple object instances, which manifests in unsatisfactory detection performance.
no code implementations • CVPR 2024 • Petru-Daniel Tudosiu, Yongxin Yang, Shifeng Zhang, Fei Chen, Steven McDonagh, Gerasimos Lampouras, Ignacio Iacobacci, Sarah Parisot
To build MuLAn, we developed a training free pipeline which decomposes a monocular RGB image into a stack of RGBA layers comprising of background and isolated instances.
no code implementations • 28 Nov 2023 • Bowen Li, Yongxin Yang, Steven McDonagh, Shifeng Zhang, Petru-Daniel Tudosiu, Sarah Parisot
Image editing affords increased control over the aesthetics and content of generated images.
no code implementations • CVPR 2023 • 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.
no code implementations • 9 Nov 2022 • William Thong, Jose Costa Pereira, Sarah Parisot, Ales Leonardis, Steven McDonagh
This restricts the diversity and number of image pairs that the model is exposed to during training.
1 code implementation • 7 Oct 2022 • Eli Verwimp, Kuo Yang, Sarah Parisot, Hong Lanqing, Steven McDonagh, Eduardo Pérez-Pellitero, Matthias De Lange, Tinne Tuytelaars
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 classification and object detection.
no code implementations • 4 Apr 2022 • Eli Verwimp, Kuo Yang, Sarah Parisot, Hong Lanqing, Steven McDonagh, Eduardo Pérez-Pellitero, Matthias De Lange, Tinne Tuytelaars
Training models continually to detect and classify objects, from new classes and new domains, remains an open problem.
no code implementations • CVPR 2022 • Sarah Parisot, Pedro M. Esperanca, Steven McDonagh, Tamas J. Madarasz, Yongxin Yang, Zhenguo Li
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.
no code implementations • 11 Jun 2021 • Mateusz Michalkiewicz, Stavros Tsogkas, Sarah Parisot, Mahsa Baktashmotlagh, Anders Eriksson, Eugene Belilovsky
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 the output space.
no code implementations • 1 Jan 2021 • Xu Lan, Steven McDonagh, Shaogang Gong, Jiali Wang, Zhenguo Li, Sarah Parisot
Semi-Supervised Few-shot Learning (SS-FSL) investigates the benefit of incorporating unlabelled data in few-shot settings.
no code implementations • 5 Oct 2020 • Katarína Tóthová, Sarah Parisot, Matthew Lee, Esther Puyol-Antón, Andrew King, Marc Pollefeys, Ender Konukoglu
Surface reconstruction from magnetic resonance (MR) imaging data is indispensable in medical image analysis and clinical research.
1 code implementation • ECCV 2020 • Carlo Biffi, Steven McDonagh, Philip Torr, Ales Leonardis, Sarah Parisot
Object detection has witnessed significant progress by relying on large, manually annotated datasets.
1 code implementation • ECCV 2020 • Danai Triantafyllidou, Sean Moran, Steven McDonagh, Sarah Parisot, Gregory Slabaugh
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 tripod.
Image and Video Processing
1 code implementation • ECCV 2020 • Mateusz Michalkiewicz, Sarah Parisot, Stavros Tsogkas, Mahsa Baktashmotlagh, Anders Eriksson, Eugene Belilovsky
In this work we demonstrate experimentally that naive baselines do not apply when the goal is to learn to reconstruct novel objects using very few examples, and that in a \emph{few-shot} learning setting, the network must learn concepts that can be applied to new categories, avoiding rote memorization.
2 code implementations • CVPR 2020 • Sean Moran, Pierre Marza, Steven McDonagh, Sarah Parisot, Gregory Slabaugh
We introduce a deep neural network, dubbed Deep Local Parametric Filters (DeepLPF), which regresses the parameters of these spatially localized filters that are then automatically applied to enhance the image.
Ranked #8 on
Image Enhancement
on MIT-Adobe 5k
(SSIM on proRGB metric)
1 code implementation • CVPR 2020 • Matthias De Lange, Xu Jia, Sarah Parisot, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars
This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device, with desirable properties regarding scalability and privacy constraints.
1 code implementation • CVPR 2020 • Daniel Hernandez-Juarez, Sarah Parisot, Benjamin Busam, Ales Leonardis, Gregory Slabaugh, Steven McDonagh
Firstly, we select a set of candidate scene illuminants in a data-driven fashion and apply them to a target image to generate of set of corrected images.
no code implementations • 18 Feb 2020 • Linpu Fang, Hang Xu, Zhili Liu, Sarah Parisot, Zhenguo Li
In this paper, we study the hybrid-supervised object detection problem, aiming to train a high quality detector with only a limited amount of fullyannotated data and fully exploiting cheap data with imagelevel labels.
1 code implementation • 18 Sep 2019 • Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh, Tinne Tuytelaars
Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase.
no code implementations • 28 Nov 2018 • Steven McDonagh, Sarah Parisot, Fengwei Zhou, Xing Zhang, Ales Leonardis, Zhenguo Li, Gregory Slabaugh
In this work, we propose a new approach that affords fast adaptation to previously unseen cameras, and robustness to changes in capture device by leveraging annotated samples across different cameras and datasets.
no code implementations • 30 Jul 2018 • Katarína Tóthová, Sarah Parisot, Matthew C. H. Lee, Esther Puyol-Antón, Lisa M. Koch, Andrew P. King, Ender Konukoglu, Marc Pollefeys
Surface reconstruction is a vital tool in a wide range of areas of medical image analysis and clinical research.
1 code implementation • NeurIPS 2018 • Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot
Our method combines a graph learner module, which learns a question specific graph representation of the input image, with the recent concept of graph convolutions, aiming to learn image representations that capture question specific interactions.
1 code implementation • 5 Jun 2018 • Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante, Matthew Lee, Ricardo Guerrero, Ben Glocker, Daniel Rueckert
Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph.
no code implementations • 29 Mar 2017 • Sofia Ira Ktena, Salim Arslan, Sarah Parisot, Daniel Rueckert
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 arises due to development or disease.
1 code implementation • 8 Mar 2017 • Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante, Matthew Lee, Ricardo Guerrerro Moreno, Ben Glocker, Daniel Rueckert
We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks.
3 code implementations • 7 Mar 2017 • Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante, Martin Rajchl, Matthew Lee, Ben Glocker, Daniel Rueckert
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 or interactions between elements.
no code implementations • 15 Nov 2016 • Sofia Ira Ktena, Sarah Parisot, Jonathan Passerat-Palmbach, Daniel Rueckert
In this work we explore a method based on graph edit distance for evaluating graph similarity, when correspondences between network elements are unknown due to different underlying subdivisions of the brain.
no code implementations • 10 Nov 2016 • Sarah Parisot, Jonathan Passerat-Palmbach, Markus D. Schirmer, Boris Gutman
Understanding brain connectivity in a network-theoretic context has shown much promise in recent years.