1.7k citations · 2.7k across the 20 of their papers we have counts for
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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…