Zobrazeno 1 - 10
of 100
pro vyhledávání: '"You, Yurong"'
Autor:
Chen, Xiangyu, Liu, Zhenzhen, Luo, Katie Z, Datta, Siddhartha, Polavaram, Adhitya, Wang, Yan, You, Yurong, Li, Boyi, Pavone, Marco, Chao, Wei-Lun, Campbell, Mark, Hariharan, Bharath, Weinberger, Kilian Q.
Ensuring robust 3D object detection and localization is crucial for many applications in robotics and autonomous driving. Recent models, however, face difficulties in maintaining high performance when applied to domains with differing sensor setups o
Externí odkaz:
http://arxiv.org/abs/2405.16034
Autor:
Cho, Jang Hyun, Ivanovic, Boris, Cao, Yulong, Schmerling, Edward, Wang, Yue, Weng, Xinshuo, Li, Boyi, You, Yurong, Krähenbühl, Philipp, Wang, Yan, Pavone, Marco
Multi-modal large language models (MLLMs) have shown incredible capabilities in a variety of 2D vision and language tasks. We extend MLLMs' perceptual capabilities to ground and reason about images in 3-dimensional space. To that end, we first develo
Externí odkaz:
http://arxiv.org/abs/2405.03685
Autor:
You, Yurong, Phoo, Cheng Perng, Diaz-Ruiz, Carlos Andres, Luo, Katie Z, Chao, Wei-Lun, Campbell, Mark, Hariharan, Bharath, Weinberger, Kilian Q
Accurate 3D object detection is crucial to autonomous driving. Though LiDAR-based detectors have achieved impressive performance, the high cost of LiDAR sensors precludes their widespread adoption in affordable vehicles. Camera-based detectors are ch
Externí odkaz:
http://arxiv.org/abs/2404.05139
Autor:
Luo, Katie Z, Liu, Zhenzhen, Chen, Xiangyu, You, Yurong, Benaim, Sagie, Phoo, Cheng Perng, Campbell, Mark, Sun, Wen, Hariharan, Bharath, Weinberger, Kilian Q.
Recent advances in machine learning have shown that Reinforcement Learning from Human Feedback (RLHF) can improve machine learning models and align them with human preferences. Although very successful for Large Language Models (LLMs), these advancem
Externí odkaz:
http://arxiv.org/abs/2310.19080
Autor:
Pan, Tai-Yu, Ma, Chenyang, Chen, Tianle, Phoo, Cheng Perng, Luo, Katie Z, You, Yurong, Campbell, Mark, Weinberger, Kilian Q., Hariharan, Bharath, Chao, Wei-Lun
Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train. In this work, we introduce an innovative pre-training approach, Grounded Point Colorizati
Externí odkaz:
http://arxiv.org/abs/2310.14592
Autor:
Zhang, Travis, Luo, Katie, Phoo, Cheng Perng, You, Yurong, Chao, Wei-Lun, Hariharan, Bharath, Campbell, Mark, Weinberger, Kilian Q.
The rapid development of 3D object detection systems for self-driving cars has significantly improved accuracy. However, these systems struggle to generalize across diverse driving environments, which can lead to safety-critical failures in detecting
Externí odkaz:
http://arxiv.org/abs/2309.12140
Autor:
You, Yurong, Phoo, Cheng Perng, Luo, Katie Z, Zhang, Travis, Chao, Wei-Lun, Hariharan, Bharath, Campbell, Mark, Weinberger, Kilian Q.
For a self-driving car to operate reliably, its perceptual system must generalize to the end-user's environment -- ideally without additional annotation efforts. One potential solution is to leverage unlabeled data (e.g., unlabeled LiDAR point clouds
Externí odkaz:
http://arxiv.org/abs/2303.15286
Autor:
Diaz-Ruiz, Carlos A., Xia, Youya, You, Yurong, Nino, Jose, Chen, Junan, Monica, Josephine, Chen, Xiangyu, Luo, Katie, Wang, Yan, Emond, Marc, Chao, Wei-Lun, Hariharan, Bharath, Weinberger, Kilian Q., Campbell, Mark
Advances in perception for self-driving cars have accelerated in recent years due to the availability of large-scale datasets, typically collected at specific locations and under nice weather conditions. Yet, to achieve the high safety requirement, t
Externí odkaz:
http://arxiv.org/abs/2208.01166
Autor:
Li, Yingwei, Chen, Tiffany, Kabkab, Maya, Yu, Ruichi, Jing, Longlong, You, Yurong, Zhao, Hang
Estimating the distance of objects is a safety-critical task for autonomous driving. Focusing on short-range objects, existing methods and datasets neglect the equally important long-range objects. In this paper, we introduce a challenging and under-
Externí odkaz:
http://arxiv.org/abs/2206.04831
Autor:
Jing, Longlong, Yu, Ruichi, Kretzschmar, Henrik, Li, Kang, Qi, Charles R., Zhao, Hang, Ayvaci, Alper, Chen, Xu, Cower, Dillon, Li, Yingwei, You, Yurong, Deng, Han, Li, Congcong, Anguelov, Dragomir
Publikováno v:
ICRA2022
Monocular image-based 3D perception has become an active research area in recent years owing to its applications in autonomous driving. Approaches to monocular 3D perception including detection and tracking, however, often yield inferior performance
Externí odkaz:
http://arxiv.org/abs/2206.03666