Search Results for author: Henrik Kretzschmar

Found 11 papers, 5 papers with code

CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection

no code implementations17 Oct 2022 Jyh-Jing Hwang, Henrik Kretzschmar, Joshua Manela, Sean Rafferty, Nicholas Armstrong-Crews, Tiffany Chen, Dragomir Anguelov

Fusing camera and radar data is challenging, however, as each of the sensors lacks information along a perpendicular axis, that is, depth is unknown to camera and elevation is unknown to radar.

Autonomous Driving Monocular 3D Object Detection +1

Instance Segmentation with Cross-Modal Consistency

no code implementations14 Oct 2022 Alex Zihao Zhu, Vincent Casser, Reza Mahjourian, Henrik Kretzschmar, Sören Pirk

We demonstrate that this formulation encourages the models to learn embeddings that are invariant to viewpoint variations and consistent across sensor modalities.

Autonomous Driving Contrastive Learning +3

Waymo Open Dataset: Panoramic Video Panoptic Segmentation

1 code implementation15 Jun 2022 Jieru Mei, Alex Zihao Zhu, Xinchen Yan, Hang Yan, Siyuan Qiao, Yukun Zhu, Liang-Chieh Chen, Henrik Kretzschmar, Dragomir Anguelov

We therefore present the Waymo Open Dataset: Panoramic Video Panoptic Segmentation Dataset, a large-scale dataset that offers high-quality panoptic segmentation labels for autonomous driving.

Autonomous Driving Image Segmentation +3

LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for Camera-Only 3D Detection

1 code implementation15 Jun 2022 Wei-Chih Hung, Henrik Kretzschmar, Vincent Casser, Jyh-Jing Hwang, Dragomir Anguelov

The popular object detection metric 3D Average Precision (3D AP) relies on the intersection over union between predicted bounding boxes and ground truth bounding boxes.

Depth Estimation Object Detection

GradTail: Learning Long-Tailed Data Using Gradient-based Sample Weighting

no code implementations16 Jan 2022 Zhao Chen, Vincent Casser, Henrik Kretzschmar, Dragomir Anguelov

We propose GradTail, an algorithm that uses gradients to improve model performance on the fly in the face of long-tailed training data distributions.

regression

Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout

1 code implementation NeurIPS 2020 Zhao Chen, Jiquan Ngiam, Yanping Huang, Thang Luong, Henrik Kretzschmar, Yuning Chai, Dragomir Anguelov

The vast majority of deep models use multiple gradient signals, typically corresponding to a sum of multiple loss terms, to update a shared set of trainable weights.

Transfer Learning

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