Search Results for author: Lei Chu

Found 19 papers, 6 papers with code

Self-Supervised Multi-Frame Neural Scene Flow

no code implementations24 Mar 2024 Dongrui Liu, Daqi Liu, Xueqian Li, Sihao Lin, Hongwei Xie, Bing Wang, Xiaojun Chang, Lei Chu

Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown remarkable adaptability in the context of large out-of-distribution autonomous driving.

Autonomous Driving Scene Flow Estimation

Correlation-Embedded Transformer Tracking: A Single-Branch Framework

1 code implementation23 Jan 2024 Fei Xie, Wankou Yang, Chunyu Wang, Lei Chu, Yue Cao, Chao Ma, Wenjun Zeng

Thus, we reformulate the two-branch Siamese tracking as a conceptually simple, fully transformer-based Single-Branch Tracking pipeline, dubbed SBT.

Feature Correlation Visual Object Tracking

Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial Sensors

1 code implementation2 Dec 2023 Yu Zhang, Songpengcheng Xia, Lei Chu, Jiarui Yang, Qi Wu, Ling Pei

This paper introduces a novel human pose estimation approach using sparse inertial sensors, addressing the shortcomings of previous methods reliant on synthetic data.

Pose Estimation

Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport

no code implementations13 Oct 2023 Songpengcheng Xia, Lei Chu, Ling Pei, Jiarui Yang, Wenxian Yu, Robert C. Qiu

To address these challenges, we propose a novel method for joint activity segmentation and recognition with timestamp supervision, in which only a single annotated sample is needed in each activity segment.

Contrastive Learning Human Activity Recognition +1

VideoTrack: Learning To Track Objects via Video Transformer

no code implementations CVPR 2023 Fei Xie, Lei Chu, Jiahao Li, Yan Lu, Chao Ma

Existing Siamese tracking methods, which are built on pair-wise matching between two single frames, heavily rely on additional sophisticated mechanism to exploit temporal information among successive video frames, hindering them from high efficiency and industrial deployments.

Visual Tracking

Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition

no code implementations16 Aug 2022 Songpengcheng Xia, Lei Chu, Ling Pei, Wenxian Yu, Robert C. Qiu

Human activity recognition (HAR) with wearables is promising research that can be widely adopted in many smart healthcare applications.

Activity Prediction Human Activity Recognition +1

Self-Supervised Point Cloud Registration with Deep Versatile Descriptors

no code implementations25 Jan 2022 Dongrui Liu, Chuanchuan Chen, Changqing Xu, Robert Qiu, Lei Chu

In this paper, we propose to jointly use both global and local descriptors to register point clouds in a self-supervised manner, which is motivated by a critical observation that all local geometries of point clouds are transformed consistently under the same transformation.

Computational Efficiency Point cloud reconstruction +2

Learning Efficient Representations for Enhanced Object Detection on Large-scene SAR Images

no code implementations22 Jan 2022 Siyan Li, Yue Xiao, Yuhang Zhang, Lei Chu, Robert C. Qiu

It is a challenging problem to detect and recognize targets on complex large-scene Synthetic Aperture Radar (SAR) images.

object-detection Object Detection

Unsupervised Shape Completion via Deep Prior in the Neural Tangent Kernel Perspective

no code implementations19 Apr 2021 Lei Chu, Hao Pan, Wenping Wang

We present a novel approach for completing and reconstructing 3D shapes from incomplete scanned data by using deep neural networks.

MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model

no code implementations20 Sep 2020 Ling Pei, Songpengcheng Xia, Lei Chu, Fanyi Xiao, Qi Wu, Wenxian Yu, Robert Qiu

Together with the rapid development of the Internet of Things (IoT), human activity recognition (HAR) using wearable Inertial Measurement Units (IMUs) becomes a promising technology for many research areas.

Human Activity Recognition Transfer Learning

A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors

no code implementations4 Mar 2020 Fanyi Xiao, Ling Pei, Lei Chu, Danping Zou, Wenxian Yu, Yifan Zhu, Tao Li

The experimental results show that the proposed method can surprisingly converge in a few iterations and achieve an accuracy of 91. 15% on a real IMU dataset, demonstrating the efficiency and effectiveness of the proposed method.

Human Activity Recognition Transfer Learning

LEMO: Learn to Equalize for MIMO-OFDM Systems with Low-Resolution ADCs

no code implementations14 May 2019 Lei Chu, Ling Pei, Husheng Li, Robert Caiming Qiu

This paper develops a new deep neural network optimized equalization framework for massive multiple input multiple output orthogonal frequency division multiplexing (MIMOOFDM) systems that employ low-resolution analog-to-digital converters (ADCs) at the base station (BS).

A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning

1 code implementation16 Aug 2018 Fei Wen, Lei Chu, Peilin Liu, Robert C. Qiu

In recent, nonconvex regularization based sparse and low-rank recovery is of considerable interest and it in fact is a main driver of the recent progress in nonconvex and nonsmooth optimization.

BIG-bench Machine Learning Compressive Sensing +2

Efficient Nonlinear Precoding for Massive MU-MIMO Downlink Systems with 1-Bit DACs

1 code implementation24 Apr 2018 Lei Chu, Fei Wen, Lily Li, Robert Qiu

The power consumption of digital-to-analog converters (DACs) constitutes a significant proportion of the total power consumption in a massive multiuser multiple-input multiple-output (MU-MIMO) base station (BS).

Signal Processing Optimization and Control

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