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检索条件"机构=Shenzhen Key Laboratory of Robotics and Computer Vision"
493 条 记 录,以下是321-330 订阅
排序:
No One Left Behind: Real-World Federated Class-Incremental Learning
arXiv
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arXiv 2023年
作者: Dong, Jiahua Li, Hongliu Cong, Yang Sun, Gan Zhang, Yulun Van Gool, Luc The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China The Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China The University of Chinese Academy of Sciences Beijing100049 China The Department of Civil and Environmental Engineering Hong Kong Polytechnic University Hong Kong The College of Automation Science and Engineering South China University of Technology Guangzhou510640 China The Computer Vision Lab ETH Zürich Zürich8092 Switzerland
Federated learning (FL) is a hot collaborative training framework via aggregating model parameters of decentralized local clients. However, most FL methods unreasonably assume data categories of FL framework are known... 详细信息
来源: 评论
Neural Gradient Regularizer
arXiv
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arXiv 2023年
作者: Xu, Shuang Wang, Yifan Zhao, Zixiang Peng, Jiangjun Cao, Xiangyong Meng, Deyu Zhang, Yulun Timofte, Radu Van Gool, Luc School of Mathematics and Statistics Northwestern Polytechnical University Xi’an710021 China Research and Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518063 China School of Electronic and Information Engineering The Key Laboratory for Intelligent Networks and Network Security Ministry of Education Xi’an Jiaotong University Xi’an710049 China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an710049 China Computer Vision Lab ETH Zurich Zürich8092 Switzerland
Owing to its significant success, the prior imposed on gradient maps has consistently been a subject of great interest in the field of image processing. Total variation (TV), one of the most representative regularizer... 详细信息
来源: 评论
Investigate indistinguishable points in semantic segmentation of 3D point cloud
arXiv
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arXiv 2021年
作者: Xu, Mingye Zhou, Zhipeng Zhang, Junhao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China Shanghai AI Lab Shanghai China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society
This paper investigates the indistinguishable points (difficult to predict label) in semantic segmentation for large-scale 3D point clouds. The indistinguishable points consist of those located in complex boundary, po... 详细信息
来源: 评论
Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization  1
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16th European Conference on computer vision, ECCV 2020
作者: Wang, Shujun Yu, Lequan Li, Caizi Fu, Chi-Wing Heng, Pheng-Ann The Chinese University of Hong Kong Shatin Hong Kong Stanford University Stanford United States Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China
The generalization capability of neural networks across domains is crucial for real-world applications. We argue that a generalized object recognition system should well understand the relationships among different im... 详细信息
来源: 评论
EfficientFCN: Holistically-Guided Decoding for Semantic Segmentation  1
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16th European Conference on computer vision, ECCV 2020
作者: Liu, Jianbo He, Junjun Zhang, Jiawei Ren, Jimmy S. Li, Hongsheng CUHK-SenseTime Joint Laboratory The Chinese University of Hong Kong Shatin Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SenseTime Research Beijing China
Both performance and efficiency are important to semantic segmentation. State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (dilatedFCN), which adopt dilated conv... 详细信息
来源: 评论
An Improved SSD for small target detection
An Improved SSD for small target detection
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作者: Xiang Li Haibo LuoX Key Laboratory of Opt-Electronic Information Processing Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences The Key Laboratory of Image Understanding and Computer Vision
SSD is one of heuristic one-stage target detection *** it has got impressive results in general target detection,it still struggles in small-size object detection and precise *** this paper,we proposed an improved SSD... 详细信息
来源: 评论
Tactile-Based Object Pose Estimation Employing Extended Kalman Filter
Tactile-Based Object Pose Estimation Employing Extended Kalm...
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International Conference on Advanced robotics and Mechatronics (ICARM)
作者: Qiguang Lin Chaojie Yan Qiang Li Yonggen Ling Yu Zheng Wangwei Lee Zhaoliang Wan Bidan Huang Xiaofeng Liu Jiangsu Key laboratory of Special Robotic Technology College of IoT Engineering Hohai University Changzhou Jiangsu P.R. China State Key Laboratory of Industrial Control and Technology Zhejiang University Institute of Cyber-System and Control Hangzhou P.R. China Neuroinformatics Group Center for Cognitive Interaction Technology (CITEC) Bielefeld University Bielefeld Germany Tencent Robotics X Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou P.R.China
In this paper, we present a new approach to estimate the pose of an object being manipulated by a multi-fingered robotic hand. The method utilizes advanced tactile sensors with high spatial resolution to optimize the ...
来源: 评论
G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks
arXiv
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arXiv 2022年
作者: Wan, Zhongwei Yin, Yichun Zhang, Wei Shi, Jiaxin Shang, Lifeng Chen, Guangyong Jiang, Xin Liu, Qun Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Science China University of Chinese Academy of Sciences China Huawei Noah's Ark Lab Hong Kong Huawei Cloud Computing Zhejiang Lab China
Recently, domain-specific PLMs have been proposed to boost the task performance of specific domains (e.g., biomedical and computer science) by continuing to pre-train general PLMs with domain-specific corpora. However... 详细信息
来源: 评论
Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution Enhancement
arXiv
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arXiv 2021年
作者: Luo, Hongming Zhou, Fei Liao, Guangsen Qiu, Guoping College of Electronics and Information Engineering Shenzhen University China Peng Cheng Laboratory Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen Key Laboratory of Digital Creative Technology China Shenzhen Institute for Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Guangdong Shenzhen China
In real-world applications, such as sharing photos on social media platforms, images are always not only sub-sampled but also heavily compressed thus often containing various artefacts. Simple methods for enhancing th... 详细信息
来源: 评论
Dual-teacher: Integrating intra-domain and inter-domain teachers for annotation-efficient cardiac segmentation  23rd
Dual-teacher: Integrating intra-domain and inter-domain teac...
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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020
作者: Li, Kang Wang, Shujun Yu, Lequan Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong Department of Radiation Oncology Stanford University Stanford United States Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China
Medical image annotations are prohibitively time-consuming and expensive to obtain. To alleviate annotation scarcity, many approaches have been developed to efficiently utilize extra information, e.g., semi-supervised... 详细信息
来源: 评论