Federated Learning(FL)is a new computing paradigm in privacy-preserving Machine Learning(ML),where the ML model is trained in a decentralized manner by the clients,preventing the server from directly accessing privacy...Federated Learning(FL)is a new computing paradigm in privacy-preserving Machine Learning(ML),where the ML model is trained in a decentralized manner by the clients,preventing the server from directly accessing privacy-sensitive data from the clients.Unfortunately,recent advances have shown potential risks for user-level privacy breaches under the cross-silo FL framework.In this paper,we propose addressing the issue by using a three-plane framework to secure the cross-silo FL,taking advantage of the Local Differential Privacy(LDP)mechanism.The key insight here is that LDP can provide strong data privacy protection while still retaining user data statistics to preserve its high utility.Experimental results on three real-world datasets demonstrate the effectiveness of our framework.展开更多
Correction to:YOLOP:You Only Look Once for Panoptic Driving Perception DOI:10.1007/s11633-022-1339-y Authors:Dong Wu,Man-Wen Liao,Wei-Tian Zhang,Xing-Gang Wang,Xiang Bai,Wen-Qing Cheng,Wen-Yu Liu The article YOLOP:You...Correction to:YOLOP:You Only Look Once for Panoptic Driving Perception DOI:10.1007/s11633-022-1339-y Authors:Dong Wu,Man-Wen Liao,Wei-Tian Zhang,Xing-Gang Wang,Xiang Bai,Wen-Qing Cheng,Wen-Yu Liu The article YOLOP:You Only Look Once for Panoptic Driving Perception,written by Dong Wu,Man-Wen Liao,Wei-Tian Zhang,Xing-Gang Wang,Xiang Bai,Wen-Qing Cheng,Wen-Yu Liu,was originally published without Open Access.展开更多
A panoptic driving perception system is an essential part of autonomous driving.A high-precision and real-time perception system can assist the vehicle in making reasonable decisions while driving.We present a panopti...A panoptic driving perception system is an essential part of autonomous driving.A high-precision and real-time perception system can assist the vehicle in making reasonable decisions while driving.We present a panoptic driving perception network(you only look once for panoptic(YOLOP))to perform traffic object detection,drivable area segmentation,and lane detection simultaneously.It is composed of one encoder for feature extraction and three decoders to handle the specific tasks.Our model performs extremely well on the challenging BDD100K dataset,achieving state-of-the-art on all three tasks in terms of accuracy and speed.Besides,we verify the effectiveness of our multi-task learning model for joint training via ablative studies.To our best knowledge,this is the first work that can process these three visual perception tasks simultaneously in real-time on an embedded device Jetson TX2(23 FPS),and maintain excellent accuracy.To facilitate further research,the source codes and pre-trained models are released at https://github.com/hustvl/YOLOP.展开更多
基金supported by the National Key R&D Program of China under Grant 2020YFB1806904by the National Natural Science Foundation of China under Grants 61872416,62171189,62172438 and 62071192+1 种基金by the Fundamental Research Funds for the Central Universities of China under Grant 2019kfyXJJS017,31732111303,31512111310by the special fund for Wuhan Yellow Crane Talents(Excellent Young Scholar).
文摘Federated Learning(FL)is a new computing paradigm in privacy-preserving Machine Learning(ML),where the ML model is trained in a decentralized manner by the clients,preventing the server from directly accessing privacy-sensitive data from the clients.Unfortunately,recent advances have shown potential risks for user-level privacy breaches under the cross-silo FL framework.In this paper,we propose addressing the issue by using a three-plane framework to secure the cross-silo FL,taking advantage of the Local Differential Privacy(LDP)mechanism.The key insight here is that LDP can provide strong data privacy protection while still retaining user data statistics to preserve its high utility.Experimental results on three real-world datasets demonstrate the effectiveness of our framework.
文摘Correction to:YOLOP:You Only Look Once for Panoptic Driving Perception DOI:10.1007/s11633-022-1339-y Authors:Dong Wu,Man-Wen Liao,Wei-Tian Zhang,Xing-Gang Wang,Xiang Bai,Wen-Qing Cheng,Wen-Yu Liu The article YOLOP:You Only Look Once for Panoptic Driving Perception,written by Dong Wu,Man-Wen Liao,Wei-Tian Zhang,Xing-Gang Wang,Xiang Bai,Wen-Qing Cheng,Wen-Yu Liu,was originally published without Open Access.
基金supported by National Natural Science Foundation of China(Nos.61876212 and 1733007)Zhejiang Laboratory,China(No.2019NB0AB02)Hubei Province College Students Innovation and Entrepreneurship Training Program,China(No.S202010487058).
文摘A panoptic driving perception system is an essential part of autonomous driving.A high-precision and real-time perception system can assist the vehicle in making reasonable decisions while driving.We present a panoptic driving perception network(you only look once for panoptic(YOLOP))to perform traffic object detection,drivable area segmentation,and lane detection simultaneously.It is composed of one encoder for feature extraction and three decoders to handle the specific tasks.Our model performs extremely well on the challenging BDD100K dataset,achieving state-of-the-art on all three tasks in terms of accuracy and speed.Besides,we verify the effectiveness of our multi-task learning model for joint training via ablative studies.To our best knowledge,this is the first work that can process these three visual perception tasks simultaneously in real-time on an embedded device Jetson TX2(23 FPS),and maintain excellent accuracy.To facilitate further research,the source codes and pre-trained models are released at https://github.com/hustvl/YOLOP.
