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Found 15 papers, 6 papers with code
NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning
Navigating a nonholonomic robot in a cluttered environment requires extremely accurate perception and locomotion for collision avoidance.
A Virtual Reality Training System for Automotive Engines Assembly and Disassembly
A free-to-use executable file (Microsoft Windows) and open-source code are available at https://github. com/LadissonLai/SUSTech_VREngine for facilitating the development of VR systems in the automotive industry.
Vision-Based Human Pose Estimation via Deep Learning: A Survey
In this article, we provide an up-to-date and in-depth overview of the deep learning approaches in vision-based HPE.
Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification
However, it is challenging to determine the network resources and road sensor placements for multi-stage training with multi-modal datasets in multi-variant scenarios.
Phase-SLAM: Phase Based Simultaneous Localization and Mapping for Mobile Structured Light Illumination Systems
In this paper, we propose a phase based Simultaneous Localization and Mapping (Phase-SLAM) framework for fast and accurate SLI sensor pose estimation and 3D object reconstruction.
Re-ranking With Constraints on Diversified Exposures for Homepage Recommender System
The homepage recommendation on most E-commerce applications places items in a hierarchical manner, where different channels display items in different styles.
Self-Ensemling for 3D Point Cloud Domain Adaption
In SEN, a student network is kept in a collaborative manner with supervised learning and self-supervised learning, and a teacher network conducts temporal consistency to learn useful representations and ensure the quality of point clouds reconstruction.
Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization
Wireless federated learning (FL) is an emerging machine learning paradigm that trains a global parametric model from distributed datasets via wireless communications.
Capture Uncertainties in Deep Neural Networks for Safe Operation of Autonomous Driving Vehicles
Uncertainties in Deep Neural Network (DNN)-based perception and vehicle's motion pose challenges to the development of safe autonomous driving vehicles.
Joint Multi-Object Detection and Tracking with Camera-LiDAR Fusion for Autonomous Driving
Multi-object tracking (MOT) with camera-LiDAR fusion demands accurate results of object detection, affinity computation and data association in real time.
Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering
The multi-label learning module leverages a memory feature bank and assigns each image with a multi-label vector based on the similarities between the image and feature bank.
Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles
The technology of dynamic map fusion among networked vehicles has been developed to enlarge sensing ranges and improve sensing accuracies for individual vehicles.
Edge Federated Learning Via Unit-Modulus Over-The-Air Computation
Simulation results show that the proposed UMAirComp framework with PAM algorithm achieves a smaller mean square error of model parameters' estimation, training loss, and test error compared with other benchmark schemes.
Learning Centric Wireless Resource Allocation for Edge Computing: Algorithm and Experiment
Edge intelligence is an emerging network architecture that integrates sensing, communication, computing components, and supports various machine learning applications, where a fundamental communication question is: how to allocate the limited wireless resources (such as time, energy) to the simultaneous model training of heterogeneous learning tasks?
Learning Centric Power Allocation for Edge Intelligence
While machine-type communication (MTC) devices generate massive data, they often cannot process this data due to limited energy and computation power.
