no code implementations • 28 Mar 2023 • Zijian Zhou, Miaojing Shi, Holger Caesar
Existing unbiased methods tackle the long-tail problem by data/loss rebalancing to favor low-frequency relations.
no code implementations • 26 Nov 2022 • Ted Lentsch, Zimin Xia, Holger Caesar, Julian F. P. Kooij
We propose SliceMatch, which consists of ground and aerial feature extractors, feature aggregators, and a pose predictor.
1 code implementation • 8 Sep 2021 • Whye Kit Fong, Rohit Mohan, Juana Valeria Hurtado, Lubing Zhou, Holger Caesar, Oscar Beijbom, Abhinav Valada
Panoptic scene understanding and tracking of dynamic agents are essential for robots and automated vehicles to navigate in urban environments.
Ranked #1 on Panoptic Tracking on Panoptic nuScenes test
no code implementations • 22 Jun 2021 • Holger Caesar, Juraj Kabzan, Kok Seang Tan, Whye Kit Fong, Eric Wolff, Alex Lang, Luke Fletcher, Oscar Beijbom, Sammy Omari
In this work, we propose the world's first closed-loop ML-based planning benchmark for autonomous driving.
1 code implementation • 19 Oct 2020 • Yiluan Guo, Holger Caesar, Oscar Beijbom, Jonah Philion, Sanja Fidler
A high-performing object detection system plays a crucial role in autonomous driving (AD).
15 code implementations • CVPR 2020 • Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom
Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar.
Ranked #303 on 3D Object Detection on nuScenes (using extra training data)
10 code implementations • CVPR 2019 • Alex H. Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, Oscar Beijbom
These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds.
no code implementations • ICCV 2017 • Miaojing Shi, Holger Caesar, Vittorio Ferrari
We propose to help weakly supervised object localization for classes where location annotations are not available, by transferring things and stuff knowledge from a source set with available annotations.
Multiple Instance Learning Weakly Supervised Object Localization +1
10 code implementations • CVPR 2018 • Holger Caesar, Jasper Uijlings, Vittorio Ferrari
To understand stuff and things in context we introduce COCO-Stuff, which augments all 164K images of the COCO 2017 dataset with pixel-wise annotations for 91 stuff classes.
Ranked #1 on Semantic Segmentation on COCO-Stuff
1 code implementation • 26 Jul 2016 • Holger Caesar, Jasper Uijlings, Vittorio Ferrari
We propose a novel method for semantic segmentation, the task of labeling each pixel in an image with a semantic class.
Ranked #1 on Semantic Segmentation on SIFT-flow
no code implementations • 6 Jul 2015 • Holger Caesar, Jasper Uijlings, Vittorio Ferrari
Semantic segmentation is the task of assigning a class-label to each pixel in an image.
Ranked #2 on Semantic Segmentation on SIFT-flow