Search Results for author: Zhichao Li

Found 14 papers, 9 papers with code

Prediction of GPU Failures Under Deep Learning Workloads

no code implementations27 Jan 2022 Heting Liu, Zhichao Li, Cheng Tan, Rongqiu Yang, Guohong Cao, Zherui Liu, Chuanxiong Guo

To improve the precision and stability of predictions, we propose several techniques, including parallel and cascade model-ensemble mechanisms and a sliding training method.

SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking

1 code implementation18 Nov 2021 Ziqi Pang, Zhichao Li, Naiyan Wang

3D multi-object tracking (MOT) has witnessed numerous novel benchmarks and approaches in recent years, especially those under the "tracking-by-detection" paradigm.

3D Multi-Object Tracking

Comparison between safety methods control barrier function vs. reachability analysis

no code implementations24 Jun 2021 Zhichao Li

This report aims to compare two safety methods: control barrier function and Hamilton-Jacobi reachability analysis.

Unsupervised Scale-consistent Depth Learning from Video

2 code implementations25 May 2021 Jia-Wang Bian, Huangying Zhan, Naiyan Wang, Zhichao Li, Le Zhang, Chunhua Shen, Ming-Ming Cheng, Ian Reid

We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time.

Monocular Depth Estimation Monocular Visual Odometry +1

LiDAR R-CNN: An Efficient and Universal 3D Object Detector

1 code implementation CVPR 2021 Zhichao Li, Feng Wang, Naiyan Wang

LiDAR-based 3D detection in point cloud is essential in the perception system of autonomous driving.

Autonomous Driving

Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences

1 code implementation10 Mar 2021 Ziqi Pang, Zhichao Li, Naiyan Wang

The code and protocols for our benchmark and algorithm are available at https://github. com/TuSimple/LiDAR_SOT/.

Autonomous Driving Multi-Object Tracking +2

Safe Robot Navigation in Cluttered Environments using Invariant Ellipsoids and a Reference Governor

no code implementations14 May 2020 Zhichao Li, Thai Duong, Nikolay Atanasov

This paper considers the problem of safe autonomous navigation in unknown environments, relying on local obstacle sensing.

Systems and Control Robotics Systems and Control

DMLO: Deep Matching LiDAR Odometry

no code implementations8 Apr 2020 Zhichao Li, Naiyan Wang

LiDAR odometry is a fundamental task for various areas such as robotics, autonomous driving.

Autonomous Driving Pose Estimation

Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video

2 code implementations NeurIPS 2019 Jia-Wang Bian, Zhichao Li, Naiyan Wang, Huangying Zhan, Chunhua Shen, Ming-Ming Cheng, Ian Reid

To the best of our knowledge, this is the first work to show that deep networks trained using unlabelled monocular videos can predict globally scale-consistent camera trajectories over a long video sequence.

Ranked #32 on Monocular Depth Estimation on KITTI Eigen split (using extra training data)

Depth And Camera Motion Monocular Depth Estimation +1

BigDL: A Distributed Deep Learning Framework for Big Data

2 code implementations16 Apr 2018 Jason Dai, Yiheng Wang, Xin Qiu, Ding Ding, Yao Zhang, Yanzhang Wang, Xianyan Jia, Cherry Zhang, Yan Wan, Zhichao Li, Jiao Wang, Shengsheng Huang, Zhongyuan Wu, Yang Wang, Yuhao Yang, Bowen She, Dongjie Shi, Qi Lu, Kai Huang, Guoqiong Song

This paper presents BigDL (a distributed deep learning framework for Apache Spark), which has been used by a variety of users in the industry for building deep learning applications on production big data platforms.

Fraud Detection Object Detection

Temporal Modeling Approaches for Large-scale Youtube-8M Video Understanding

1 code implementation14 Jul 2017 Fu Li, Chuang Gan, Xiao Liu, Yunlong Bian, Xiang Long, Yandong Li, Zhichao Li, Jie zhou, Shilei Wen

This paper describes our solution for the video recognition task of the Google Cloud and YouTube-8M Video Understanding Challenge that ranked the 3rd place.

Frame Video Recognition +1

Dynamic Computational Time for Visual Attention

1 code implementation30 Mar 2017 Zhichao Li, Yi Yang, Xiao Liu, Feng Zhou, Shilei Wen, Wei Xu

We propose a dynamic computational time model to accelerate the average processing time for recurrent visual attention (RAM).


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