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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371-380 订阅
排序:
UDC-UNet: Under-Display Camera Image Restoration via U-shape Dynamic Network
arXiv
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arXiv 2022年
作者: Liu, Xina Hu, Jinfan Chen, Xiangyu Dong, Chao Shenzhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Shanghai China University of Macau Shanghai China Shanghai AI Laboratory Shanghai China
Under-Display Camera (UDC) has been widely exploited to help smartphones realize full-screen displays. However, as the screen could inevitably affect the light propagation process, the images captured by the UDC syste... 详细信息
来源: 评论
Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing
arXiv
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arXiv 2021年
作者: Kong, Zhe Zhang, Wentian Liu, Feng Luo, Wenhan Liu, Haozhe Shen, Linlin Ramachandra, Raghavendra College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus Guangdong Shenzhen518107 China Norwegian University of Science and Technology Gjøvik2818 Norway
Biometric systems are vulnerable to Presentation Attacks (PA) performed using various Presentation Attack Instruments (PAIs). Even though there are numerous Presentation Attack Detection (PAD) techniques based on both... 详细信息
来源: 评论
MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning
arXiv
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arXiv 2021年
作者: Zhang, David Junhao Li, Kunchang Wang, Yali Chen, Yunpeng Chandra, Shashwat Qiao, Yu Liu, Luoqi Shou, Mike Zheng National University of Singapore Singapore Meitu Inc China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Shanghai AI Laboratory China
Recently, MLP-Like networks have been revived for image recognition. However, whether it is possible to build a generic MLP-Like architecture on video domain has not been explored, due to complex spatial-temporal mode... 详细信息
来源: 评论
Deep Multi-Model Fusion for Single-Image Dehazing
Deep Multi-Model Fusion for Single-Image Dehazing
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International Conference on computer vision (ICCV)
作者: Zijun Deng Lei Zhu Xiaowei Hu Chi-Wing Fu Xuemiao Xu Qing Zhang Jing Qin Pheng-Ann Heng South China University of Technology Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology CAS The Chinese University of Hong Kong State Key Laboratory of Subtropical Building Science Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Sun Yat-sen University The Hong Kong Polytechnic University CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology CAS
This paper presents a deep multi-model fusion network to attentively integrate multiple models to separate layers and boost the performance in single-image dehazing. To do so, we first formulate the attentional featur... 详细信息
来源: 评论
Mitigating Artifacts in Real-World Video Super-Resolution Models
arXiv
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arXiv 2022年
作者: Xie, Liangbin Wang, Xintao Shi, Shuwei Gu, Jinjin Dong, Chao Shan, Ying The Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China University of Macau China ARC Lab Tencent PCG China Shenzhen International Graduate School Tsinghua University China The University of Sydney Australia Shanghai Artificial Intelligence Laboratory China
The recurrent structure is a prevalent framework for the task of video super-resolution, which models the temporal dependency between frames via hidden states. When applied to real-world scenarios with unknown and com... 详细信息
来源: 评论
Direct Shooting Method for Numerical Optimal Control: A Modified Transcription Approach
Direct Shooting Method for Numerical Optimal Control: A Modi...
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European Control Conference (ECC)
作者: Jiawei Tang Yuxing Zhong Pengyu Wang Xingzhou Chen Shuang Wu Ling Shi Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Water Bay Hong Kong SAR Department Of Electronic And Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China Noah's Ark Lab Huawei
Direct shooting is an efficient method to solve numerical optimal control. It utilizes the Runge-Kutta scheme to discretize a continuous-time optimal control problem making the problem solvable by nonlinear programmin... 详细信息
来源: 评论
Designing and Training of A Dual CNN for Image Denoising
arXiv
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arXiv 2020年
作者: Zuo, Wangmeng Zhang, David Lin, Chia-Wen Xu, Yong Tian, Chunwei Du, Bo Bio-Computing Research Center Harbin Institute of Technology Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition ShenzhenGuangdong518055 China Peng Cheng Laboratory Shenzhen518055 China School of Computer Science and Technology Harbin Institute of Technology 150001 HarbinHeilongjiang China Peng Cheng Laboratory Shenzhen518055 China School of Computer Science Wuhan University 430072 WuhanHubei China Department of Electrical Engineering Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan 518172 ShenzhenGuangdong China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China
Deep convolutional neural networks (CNNs) for image denoising have recently attracted increasing research interest. However, plain networks cannot recover fine details for a complex task, such as real noisy images. In... 详细信息
来源: 评论
Single shot text detector with regional attention
arXiv
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arXiv 2017年
作者: He, Pan Huang, Weilin He, Tong Zhu, Qile Qiao, Yu Li, Xiaolin National Science Foundation Center for Big Learning University of Florida Department of Engineering Science University of Oxford Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences
We present a novel single-shot text detector that directly outputs word-level bounding boxes in a natural image. We propose an attention mechanism which roughly identifies text regions via an automatically learned att... 详细信息
来源: 评论
AO2-DETR: Arbitrary-Oriented Object Detection Transformer
arXiv
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arXiv 2022年
作者: Dai, Linhui Liu, Hong Tang, Hao Wu, Zhiwei Song, Pinhao The Key Laboratory of Machine Perception Shenzhen Graduate School Peking University Beijing100871 China The Computer Vision Lab ETH Zurich Zurich Switzerland The School of Software Engineering South China University of Technology Guangdong Guangzhou China
Arbitrary-oriented object detection (AOOD) is a challenging task to detect objects in the wild with arbitrary orientations and cluttered arrangements. Existing approaches are mainly based on anchor-based boxes or dens... 详细信息
来源: 评论
Direction-aware spatial context features for shadow detection
arXiv
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arXiv 2017年
作者: Hu, Xiaowei Zhu, Lei Fu, Chi-Wing Qin, Jing Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Centre for Smart Health School of Nursing Hong Kong Polytechnic University Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China
Shadow detection is a fundamental and challenging task, since it requires an understanding of global image semantics and there are various backgrounds around shadows. This paper presents a novel network for shadow det... 详细信息
来源: 评论