21 citations · 84 across the 25 of their papers we have counts for
6 papers · 1 filter
Detect Everything with Few Examples
Xinyu Zhang, Yuhan Liu, Yuting Wang +1
Few-shot object detection aims at detecting novel categories given only a few example images. It is a basic skill for a robot to perform tasks in open environments. Recent methods…
Optical Flow boosts Unsupervised Localization and Segmentation
Xinyu Zhang, Abdeslam Boularias
Unsupervised localization and segmentation are long-standing robot vision challenges that describe the critical ability for an autonomous robot to learn to decompose images into in…
Self-Supervised Learning of Object Segmentation from Unlabeled RGB-D Videos
Shiyang Lu, Yunfu Deng, Abdeslam Boularias +1
This work proposes a self-supervised learning system for segmenting rigid objects in RGB images. The proposed pipeline is trained on unlabeled RGB-D videos of static objects, which…
A Self-supervised Learning System for Object Detection in Videos Using Random Walks on Graphs
Juntao Tan, Changkyu Song, Abdeslam Boularias
This paper presents a new self-supervised system for learning to detect novel and previously unseen categories of objects in images. The proposed system receives as input several u…
Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images
Jean-Philippe Mercier, Chaitanya Mitash, Philippe Giguère +1
This work proposes a process for efficiently training a point-wise object detector that enables localizing objects and computing their 6D poses in cluttered and occluded scenes. Ac…
Robust 6D Object Pose Estimation with Stochastic Congruent Sets
Chaitanya Mitash, Abdeslam Boularias, Kostas Bekris
Object pose estimation is frequently achieved by first segmenting an RGB image and then, given depth data, registering the corresponding point cloud segment against the object's 3D…