145 citations · 224 across the 24 of their papers we have counts for
5 papers · 1 filter
SF-UDA: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection
Cristiano Saltori, Stéphane Lathuiliére, Nicu Sebe +2
3D object detectors based only on LiDAR point clouds hold the state-of-the-art on modern street-view benchmarks. However, LiDAR-based detectors poorly generalize across domains due…
Learning to Cluster under Domain Shift
Willi Menapace, Stéphane Lathuilière, Elisa Ricci
While unsupervised domain adaptation methods based on deep architectures have achieved remarkable success in many computer vision tasks, they rely on a strong assumption, i.e. labe…
Online Continual Learning under Extreme Memory Constraints
Enrico Fini, Stéphane Lathuilière, Enver Sangineto +2
Continual Learning (CL) aims to develop agents emulating the human ability to sequentially learn new tasks while being able to retain knowledge obtained from past experiences. In t…
Motion-supervised Co-Part Segmentation
Aliaksandr Siarohin, Subhankar Roy, Stéphane Lathuilière +3
Recent co-part segmentation methods mostly operate in a supervised learning setting, which requires a large amount of annotated data for training. To overcome this limitation, we p…
First Order Motion Model for Image Animation
Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov +2
Image animation consists of generating a video sequence so that an object in a source image is animated according to the motion of a driving video. Our framework addresses this pro…