Search Results for author: Yuwen Xiong

Found 27 papers, 8 papers with code

Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications

1 code implementation11 Jan 2024 Yuwen Xiong, Zhiqi Li, Yuntao Chen, Feng Wang, Xizhou Zhu, Jiapeng Luo, Wenhai Wang, Tong Lu, Hongsheng Li, Yu Qiao, Lewei Lu, Jie zhou, Jifeng Dai

The advancements in speed and efficiency of DCNv4, combined with its robust performance across diverse vision tasks, show its potential as a foundational building block for future vision models.

Image Classification Image Generation +1

LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds

no code implementations2 Nov 2023 Anqi Joyce Yang, Sergio Casas, Nikita Dvornik, Sean Segal, Yuwen Xiong, Jordan Sir Kwang Hu, Carter Fang, Raquel Urtasun

Auto-labels are most commonly generated via a two-stage approach -- first objects are detected and tracked over time, and then each object trajectory is passed to a learned refinement model to improve accuracy.

Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation

no code implementations2 Nov 2023 Jay Sarva, Jingkang Wang, James Tu, Yuwen Xiong, Sivabalan Manivasagam, Raquel Urtasun

In this paper, we propose a framework, Adv3D, that takes real world scenarios and performs closed-loop sensor simulation to evaluate autonomy performance, and finds vehicle shapes that make the scenario more challenging, resulting in autonomy failures and uncomfortable SDV maneuvers.

Motion Planning

UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generation

no code implementations2 Nov 2023 Yuwen Xiong, Wei-Chiu Ma, Jingkang Wang, Raquel Urtasun

We show that by aligning the representation of a sparse point cloud to that of a dense point cloud, we can densify the sparse point clouds as if they were captured by a real high-density LiDAR, drastically reducing the cost.

Rethinking Closed-loop Training for Autonomous Driving

no code implementations27 Jun 2023 Chris Zhang, Runsheng Guo, Wenyuan Zeng, Yuwen Xiong, Binbin Dai, Rui Hu, Mengye Ren, Raquel Urtasun

Recent advances in high-fidelity simulators have enabled closed-loop training of autonomous driving agents, potentially solving the distribution shift in training v. s.

Autonomous Driving

Learning Compact Representations for LiDAR Completion and Generation

no code implementations CVPR 2023 Yuwen Xiong, Wei-Chiu Ma, Jingkang Wang, Raquel Urtasun

We show that by aligning the representation of a sparse point cloud to that of a dense point cloud, we can densify the sparse point clouds as if they were captured by a real high-density LiDAR, drastically reducing the cost.

Network Automatic Pruning: Start NAP and Take a Nap

no code implementations17 Jan 2021 Wenyuan Zeng, Yuwen Xiong, Raquel Urtasun

This process is typically time-consuming and requires expert knowledge to achieve good results.

Network Pruning

Cost-Efficient Online Hyperparameter Optimization

no code implementations17 Jan 2021 Jingkang Wang, Mengye Ren, Ilija Bogunovic, Yuwen Xiong, Raquel Urtasun

Recent work on hyperparameters optimization (HPO) has shown the possibility of training certain hyperparameters together with regular parameters.

Bayesian Optimization Hyperparameter Optimization

Self-Supervised Representation Learning from Flow Equivariance

no code implementations ICCV 2021 Yuwen Xiong, Mengye Ren, Wenyuan Zeng, Raquel Urtasun

Motivated by this ability, we present a new self-supervised learning representation framework that can be directly deployed on a video stream of complex scenes with many moving objects.

Instance Segmentation object-detection +5

Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting

no code implementations7 Jan 2021 Katie Luo, Sergio Casas, Renjie Liao, Xinchen Yan, Yuwen Xiong, Wenyuan Zeng, Raquel Urtasun

On two large-scale real-world datasets, nuScenes and ATG4D, we showcase that our scene-occupancy predictions are more accurate and better calibrated than those from state-of-the-art motion forecasting methods, while also matching their performance in pedestrian motion forecasting metrics.

Motion Forecasting

Weakly-supervised 3D Shape Completion in the Wild

no code implementations ECCV 2020 Jiayuan Gu, Wei-Chiu Ma, Sivabalan Manivasagam, Wenyuan Zeng, ZiHao Wang, Yuwen Xiong, Hao Su, Raquel Urtasun

3D shape completion for real data is important but challenging, since partial point clouds acquired by real-world sensors are usually sparse, noisy and unaligned.

Point Cloud Registration Pose Estimation

LoCo: Local Contrastive Representation Learning

no code implementations NeurIPS 2020 Yuwen Xiong, Mengye Ren, Raquel Urtasun

Deep neural nets typically perform end-to-end backpropagation to learn the weights, a procedure that creates synchronization constraints in the weight update step across layers and is not biologically plausible.

Contrastive Learning Instance Segmentation +4

LevelSet R-CNN: A Deep Variational Method for Instance Segmentation

no code implementations30 Jul 2020 Namdar Homayounfar, Yuwen Xiong, Justin Liang, Wei-Chiu Ma, Raquel Urtasun

Obtaining precise instance segmentation masks is of high importance in many modern applications such as robotic manipulation and autonomous driving.

Autonomous Driving Instance Segmentation +2

PolyTransform: Deep Polygon Transformer for Instance Segmentation

no code implementations CVPR 2020 Justin Liang, Namdar Homayounfar, Wei-Chiu Ma, Yuwen Xiong, Rui Hu, Raquel Urtasun

In this paper, we propose PolyTransform, a novel instance segmentation algorithm that produces precise, geometry-preserving masks by combining the strengths of prevailing segmentation approaches and modern polygon-based methods.

Ranked #1000000000 on Instance Segmentation on Cityscapes test (using extra training data)

Instance Segmentation Segmentation +2

Learning to Remember from a Multi-Task Teacher

no code implementations10 Oct 2019 Yuwen Xiong, Mengye Ren, Raquel Urtasun

Recent studies on catastrophic forgetting during sequential learning typically focus on fixing the accuracy of the predictions for a previously learned task.


Inference in Probabilistic Graphical Models by Graph Neural Networks

1 code implementation21 Mar 2018 KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, Xaq Pitkow

Message-passing algorithms, such as belief propagation, are a natural way to disseminate evidence amongst correlated variables while exploiting the graph structure, but these algorithms can struggle when the conditional dependency graphs contain loops.

Decision Making

Reviving and Improving Recurrent Back-Propagation

1 code implementation ICML 2018 Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard Zemel

We examine all RBP variants along with BPTT and TBPTT in three different application domains: associative memory with continuous Hopfield networks, document classification in citation networks using graph neural networks and hyperparameter optimization for fully connected networks.

Document Classification Hyperparameter Optimization

Deformable Convolutional Networks

38 code implementations ICCV 2017 Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, Yichen Wei

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules.

Object Detection Semantic Segmentation +1

Deep Feature Flow for Video Recognition

3 code implementations CVPR 2017 Xizhou Zhu, Yuwen Xiong, Jifeng Dai, Lu Yuan, Yichen Wei

Yet, it is non-trivial to transfer the state-of-the-art image recognition networks to videos as per-frame evaluation is too slow and unaffordable.

Video Recognition Video Semantic Segmentation

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