34 citations · 94 across the 8 of their papers we have counts for
6 papers · 1 filter
docExtractor: An off-the-shelf historical document element extraction
Tom Monnier, Mathieu Aubry
We present docExtractor, a generic approach for extracting visual elements such as text lines or illustrations from historical documents without requiring any real data annotation.…
Learning to Guide Local Feature Matches
François Darmon, Mathieu Aubry, Pascal Monasse
We tackle the problem of finding accurate and robust keypoint correspondences between images. We propose a learning-based approach to guide local feature matches via a learned appr…
CosyPose: Consistent multi-view multi-object 6D pose estimation
Yann Labbé, Justin Carpentier, Mathieu Aubry +1
We introduce an approach for recovering the 6D pose of multiple known objects in a scene captured by a set of input images with unknown camera viewpoints. First, we present a singl…
Impact of base dataset design on few-shot image classification
Othman Sbai, Camille Couprie, Mathieu Aubry
The quality and generality of deep image features is crucially determined by the data they have been trained on, but little is known about this often overlooked effect. In this pap…
Deep Transformation-Invariant Clustering
Tom Monnier, Thibault Groueix, Mathieu Aubry
Recent advances in image clustering typically focus on learning better deep representations. In contrast, we present an orthogonal approach that does not rely on abstract features…
RANSAC-Flow: generic two-stage image alignment
Xi Shen, François Darmon, Alexei A. Efros +1
This paper considers the generic problem of dense alignment between two images, whether they be two frames of a video, two widely different views of a scene, two paintings depictin…