Search Results for author: Kelvin Wong

Found 11 papers, 2 papers with code

Patcher: Patch Transformers with Mixture of Experts for Precise Medical Image Segmentation

no code implementations3 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.

Lesion Segmentation Semantic Segmentation

Non-parametric Memory for Spatio-Temporal Segmentation of Construction Zones for Self-Driving

no code implementations18 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).

SceneGen: Learning to Generate Realistic Traffic Scenes

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.

MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models

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.

LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World

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.

Identifying Unknown Instances for Autonomous Driving

no code implementations24 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.

Autonomous Driving Instance Segmentation +1

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