60 citations · 75 across the 9 of their papers we have counts for
15 papers · 1 filter
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…
CroMo: Cross-Modal Learning for Monocular Depth Estimation
Yannick Verdié, Jifei Song, Barnabé Mas +3
Learning-based depth estimation has witnessed recent progress in multiple directions; from self-supervision using monocular video to supervised methods offering highest accuracy. C…
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…
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…
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…