2 citations · 2 across the 5 of their papers we have counts for
5 papers · 1 filter
PoM: A Linear-Time Replacement for Attention with the Polynomial Mixer
David Picard, Nicolas Dufour, Lucas Degeorge +14
This paper introduces the Polynomial Mixer (PoM), a novel token mixing mechanism with linear complexity that serves as a drop-in replacement for self-attention. PoM aggregates inpu…
sim2art: Accurate Articulated Object Modeling from a Single Video using Synthetic Training Data Only
Arslan Artykov, Tom Ravaud, Corentin Sautier +1
Understanding articulated objects from monocular video is a crucial yet challenging task in robotics and digital twin creation. Existing methods often rely on complex multi-view se…
Is clustering enough for LiDAR instance segmentation? A state-of-the-art training-free baseline
Corentin Sautier, Gilles Puy, Alexandre Boulch +2
Panoptic segmentation of LiDAR point clouds is fundamental to outdoor scene understanding, with autonomous driving being a primary application. While state-of-the-art approaches ty…
UNIT: Unsupervised Online Instance Segmentation through Time
Corentin Sautier, Gilles Puy, Alexandre Boulch +2
Online object segmentation and tracking in Lidar point clouds enables autonomous agents to understand their surroundings and make safe decisions. Unfortunately, manual annotations…
Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data
Corentin Sautier, Gilles Puy, Spyros Gidaris +3
Segmenting or detecting objects in sparse Lidar point clouds are two important tasks in autonomous driving to allow a vehicle to act safely in its 3D environment. The best performi…