no code implementations • 26 Sep 2024 • Chris Zhang, Sourav Biswas, Kelvin Wong, Kion Fallah, Lunjun Zhang, Dian Chen, Sergio Casas, Raquel Urtasun
Large-scale data is crucial for learning realistic and capable driving policies.
no code implementations • 22 Jan 2024 • Chao Liu, Boxi Chen, Wei Shao, Chris Zhang, Kelvin Wong, Yi Zhang
Through our comprehensive review and analysis, this paper seeks to contribute to the ongoing discourse on ML-based IoT security, offering valuable insights and practical solutions to secure ML models and data in the rapidly expanding field of artificial intelligence in IoT.
no code implementations • 2 Nov 2023 • Chris Zhang, James Tu, Lunjun Zhang, Kelvin Wong, Simon Suo, Raquel Urtasun
Our experiments show that RTR learns more realistic and generalizable traffic simulation policies, achieving significantly better tradeoffs between human-like driving and traffic compliance in both nominal and long-tail scenarios.
no code implementations • CVPR 2023 • Simon Suo, Kelvin Wong, Justin Xu, James Tu, Alexander Cui, Sergio Casas, Raquel Urtasun
Towards this goal, we propose to leverage the wealth of interesting scenarios captured in the real world and make them reactive and controllable to enable closed-loop SDV evaluation in what-if situations.
no code implementations • 4 Nov 2022 • Alexander Cui, Sergio Casas, Kelvin Wong, Simon Suo, Raquel Urtasun
However, this approach is computationally expensive for multi-agent prediction as inference needs to be run for each agent.
1 code implementation • 30 Jun 2022 • Haomiao Ni, Yuan Xue, Kelvin Wong, John Volpi, Stephen T. C. Wong, James Z. Wang, Xiaolei Huang
In this paper, we propose a novel Asymmetry Disentanglement Network (ADN) to automatically separate pathological asymmetries and intrinsic anatomical asymmetries in NCCTs for more effective and interpretable AIS segmentation.
1 code implementation • 3 Jun 2022 • Yanglan Ou, Ye Yuan, Xiaolei Huang, Stephen T. C. Wong, John Volpi, James Z. Wang, Kelvin Wong
We also propose a new mixture-of-experts (MoE) based decoder, which treats the feature maps from the encoder as experts and selects a suitable set of expert features to predict the label for each pixel.
no code implementations • 24 Sep 2021 • Tongan Cai, Haomiao Ni, Mingli Yu, Xiaolei Huang, Kelvin Wong, John Volpi, James Z. Wang, Stephen T. C. Wong
In an emergency room (ER) setting, stroke triage or screening is a common challenge.
1 code implementation • 28 Apr 2021 • Yanglan Ou, Ye Yuan, Xiaolei Huang, Kelvin Wong, John Volpi, James Z. Wang, Stephen T. C. Wong
Thus, it is not ideal to apply most existing segmentation methods as they are designed for either 2D or 3D images.
no code implementations • 18 Jan 2021 • Min Bai, Shenlong Wang, Kelvin Wong, Ersin Yumer, Raquel Urtasun
In this paper, we introduce a non-parametric memory representation for spatio-temporal segmentation that captures the local space and time around an autonomous vehicle (AV).
no code implementations • CVPR 2021 • Shuhan Tan, Kelvin Wong, Shenlong Wang, Sivabalan Manivasagam, Mengye Ren, Raquel Urtasun
Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics and are limited in their ability to model the true complexity and diversity of real traffic scenes, thus inducing a content gap between synthesized traffic scenes versus real ones.
no code implementations • NeurIPS 2020 • Sourav Biswas, Jerry Liu, Kelvin Wong, Shenlong Wang, Raquel Urtasun
Our model exploits spatio-temporal relationships across multiple LiDAR sweeps to reduce the bitrate of both geometry and intensity values.
no code implementations • ECCV 2020 • Kelvin Wong, Qiang Zhang, Ming Liang, Bin Yang, Renjie Liao, Abbas Sadat, Raquel Urtasun
We present a novel method for testing the safety of self-driving vehicles in simulation.
no code implementations • CVPR 2020 • Sivabalan Manivasagam, Shenlong Wang, Kelvin Wong, Wenyuan Zeng, Mikita Sazanovich, Shuhan Tan, Bin Yang, Wei-Chiu Ma, Raquel Urtasun
We first utilize ray casting over the 3D scene and then use a deep neural network to produce deviations from the physics-based simulation, producing realistic LiDAR point clouds.
6 code implementations • CVPR 2020 • Lila Huang, Shenlong Wang, Kelvin Wong, Jerry Liu, Raquel Urtasun
We present a novel deep compression algorithm to reduce the memory footprint of LiDAR point clouds.
no code implementations • 24 Oct 2019 • Kelvin Wong, Shenlong Wang, Mengye Ren, Ming Liang, Raquel Urtasun
In the past few years, we have seen great progress in perception algorithms, particular through the use of deep learning.
no code implementations • 30 Jul 2019 • Yuwen Xiong, Mengye Ren, Renjie Liao, Kelvin Wong, Raquel Urtasun
Point clouds are the native output of many real-world 3D sensors.