29 citations · 44 across the 6 of their papers we have counts for
8 papers
CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for Robust 3D Object Detection
Jyh-Jing Hwang, Henrik Kretzschmar, Joshua Manela +4
Robust 3D object detection is critical for safe autonomous driving. Camera and radar sensors are synergistic as they capture complementary information and work well under different…
Instance Segmentation with Cross-Modal Consistency
Alex Zihao Zhu, Vincent Casser, Reza Mahjourian +2
Segmenting object instances is a key task in machine perception, with safety-critical applications in robotics and autonomous driving. We introduce a novel approach to instance seg…
Block-NeRF: Scalable Large Scene Neural View Synthesis
Matthew Tancik, Vincent Casser, Xinchen Yan +5
We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments. Specifically, we demonstrate that when scaling NeRF to render city-scale sce…
GradTail: Learning Long-Tailed Data Using Gradient-based Sample Weighting
Zhao Chen, Vincent Casser, Henrik Kretzschmar +1
We propose GradTail, an algorithm that uses gradients to improve model performance on the fly in the face of long-tailed training data distributions. Unlike conventional long-tail…
Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout
Zhao Chen, Jiquan Ngiam, Yanping Huang +4
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. However, these…
SoDA: Multi-Object Tracking with Soft Data Association
Wei-Chih Hung, Henrik Kretzschmar, Tsung-Yi Lin +4
Robust multi-object tracking (MOT) is a prerequisite fora safe deployment of self-driving cars. Tracking objects, however, remains a highly challenging problem, especially in clutt…