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检索条件"机构=Shenzhen Key Laboratory of Robotics and Computer Vision"
498 条 记 录,以下是291-300 订阅
排序:
Activating More Pixels in Image Super-Resolution Transformer
arXiv
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arXiv 2022年
作者: Chen, Xiangyu Wang, Xintao Zhou, Jiantao Qiao, Yu Dong, Chao State Key Laboratory of Internet of Things for Smart City University of Macau China Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Shanghai Artificial Intelligence Laboratory China ARC Lab Tencent PCG China
Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. However, we find that these networks can only utilize a limited spatial range of input information... 详细信息
来源: 评论
DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching
arXiv
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arXiv 2024年
作者: Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China
Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline invol... 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
Corrigendum to “PSFHS challenge report: pubic symphysis and fetal head segmentation from intrapartum ultrasound images” [Medical Image Analysis 99 (2025),103353]
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Medical Image Analysis 2025年
作者: Jieyun Bai Zihao Zhou Zhanhong Ou Gregor Koehler Raphael Stock Klaus Maier-Hein Marawan Elbatel Robert Martí Xiaomeng Li Yaoyang Qiu Panjie Gou Gongping Chen Lei Zhao Jianxun Zhang Yu Dai Fangyijie Wang Guénolé Silvestre Kathleen Curran Hongkun Sun Jing Xu Karim Lekadir Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China Auckland Bioengineering Institute The University of Auckland Auckland New Zealand Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Department of Computer Science and Engineering The Chinese University of Hong Kong China Computer Vision and Robotics Group University of Girona Girona Spain Canon Medical Systems (China) Co. LTD Beijing China College of Artificial Intelligence Nankai University Tianjin China College of Computer Science and Electronic Engineering Hunan University Changsha China School of Medicine University College Dublin Dublin Ireland School of Computer Science University College Dublin Dublin Ireland School of Statistics & Mathematics Zhejiang Gongshang University Hangzhou China Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Institució Catalana de Recerca i Estudis Avançats (ICREA) Barcelona Spain
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
Mx2M: Masked Cross-Modality Modeling in Domain Adaptation for 3D Semantic Segmentation
arXiv
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arXiv 2023年
作者: Zhang, Boxiang Wang, Zunran Ling, Yonggen Guan, Yuanyuan Zhang, Shenghao Li, Wenhui College of Computer Science and Technology Jilin University Changchun China Robotics X Tencent Shenzhen China Key Laboratory of Symbolic Computation and Knowledge Engineer Jilin University Changchun China
Existing methods of cross-modal domain adaptation for 3D semantic segmentation predict results only via 2D-3D complementarity that is obtained by cross-modal feature matching. However, as lacking supervision in the ta... 详细信息
来源: 评论
EfficientFCN: Holistically-guided decoding for semantic segmentation
arXiv
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arXiv 2020年
作者: Liu, Jianbo He, Junjun Zhang, Jiawei Ren, Jimmy S. Li, Hongsheng CUHK-SenseTime Joint Laboratory Chinese University of Hong Kong Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China SenseTime Research 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
OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer
OSRT: Omnidirectional Image Super-Resolution with Distortion...
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Conference on computer vision and Pattern Recognition (CVPR)
作者: Fanghua Yu Xintao Wang Mingdeng Cao Gen Li Ying Shan Chao Dong ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ARC Tencent PCG The University of Tokyo Platform Technologies Tencent Online Video Shanghai Artificial Intelligence Laboratory
Omnidirectional images (ODIs) have obtained lots of research interest for immersive experiences. Although ODIs require extremely high resolution to capture details of the entire scene, the resolutions of most ODIs are...
来源: 评论
GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features
arXiv
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arXiv 2022年
作者: Song, Siyang Song, Yuxin Luo, Cheng Song, Zhiyuan Kuzucu, Selim Jia, Xi Guo, Zhijiang Xie, Weicheng Shen, Linlin Gunes, Hatice The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FT United Kingdom Baidu Inc Beijing100193 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Department of Computer Engineering Middle East Technical University Ankara Turkey School of Computer Science University of Birmingham Birmingham United Kingdom
Graph is powerful for representing various types of real-world data. The topology (edges’ presence) and edges’ features of a graph decides the message passing mechanism among vertices within the graph. While most ex... 详细信息
来源: 评论
A unified leader-follower scheme for mobile robots with uncalibrated on-board camera
A unified leader-follower scheme for mobile robots with unca...
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2017 IEEE International Conference on robotics and Automation, ICRA 2017
作者: Guo, Dejun Wang, Hesheng Chen, Weidong Liu, Ming Xia, Zeyang Leang, Kam K. Lab University of Utah Robotics Center Salt Lake CityUT84112 United States Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of Electronics and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Institute of Biomedical and Health Engineering Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China
This paper studies the problem of image-based leader-follower formation control for mobile robots, where the controller is designed independently of the leader's motion. An adaptive control scheme, which is suitab... 详细信息
来源: 评论