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检索条件"任意字段=2006 Conference on Computer Vision and Pattern Recognition Workshops"
5524 条 记 录,以下是491-500 订阅
Trust Your IMU: Consequences of Ignoring the IMU Drift
Trust Your IMU: Consequences of Ignoring the IMU Drift
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Ornhag, Marcus Valtonen Persson, Patrik Wadenback, Marten Astrom, Kalle Heyden, Anders Lund Univ Ctr Math Sci Lund Sweden Linkoping Univ Dept Elect Engn Linkoping Sweden
In this paper, we argue that modern pre-integration methods for inertial measurement units (IMUs) are accurate enough to ignore the drift for short time intervals. This allows us to consider a simplified camera model,... 详细信息
来源: 评论
Dual Attention Poser: Dual Path Body Tracking Based on Attention
Dual Attention Poser: Dual Path Body Tracking Based on Atten...
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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023
作者: Di, Xinhan Dai, Xiaokun Zhang, Xinkang Chen, Xinrong Fudan Universiry Academy for Engineering&Technology China Deepearthgo Fudan University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China
Currently, mixed reality head-mounted displays tracking the full body of users is an important human-computer interaction mode through the pose of the head and the hands. Unfortunately, users' virtual representati... 详细信息
来源: 评论
Denoising diffusion models for out-of-distribution detection
Denoising diffusion models for out-of-distribution detection
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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023
作者: Graham, Mark S. Pinaya, Walter H. L. Tudosiu, Petru-Daniel Nachev, Parashkev Ourselin, Sebastien Cardoso, M. Jorge King's College London United Kingdom University College London United Kingdom
Out-of-distribution detection is crucial to the safe deployment of machine learning systems. Currently, unsupervised out-of-distribution detection is dominated by generative-based approaches that make use of estimates... 详细信息
来源: 评论
A Hybrid Network of CNN and Transformer for Lightweight Image Super-Resolution
A Hybrid Network of CNN and Transformer for Lightweight Imag...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Fang, Jinsheng Lin, Hanjiang Chen, Xinyu Zeng, Kun Minnan Normal Univ Zhangzhou Peoples R China Minjiang Univ Fuzhou Fujian Peoples R China
Recently, a number of CNN based methods have made great progress in single image super-resolution. However, these existing architectures commonly build massive number of network layers, bringing high computational com... 详细信息
来源: 评论
Blueprint Separable Residual Network for Efficient Image Super-Resolution
Blueprint Separable Residual Network for Efficient Image Sup...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Gu, Jinjin Qiao, Yu Dong, Chao Chinese Acad Sci ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol SIAT SenseTime Joint Lab Beijing Peoples R China Univ Macau Taipa Macao Peoples R China Shanghai AI Lab Shanghai Peoples R China Univ Sydney Sydney NSW Australia
Recent advances in single image super-resolution (SISR) have achieved extraordinary performance, but the computational cost is too heavy to apply in edge devices. To alleviate this problem, many novel and effective so... 详细信息
来源: 评论
IMDeception: Grouped Information Distilling Super-Resolution Network
IMDeception: Grouped Information Distilling Super-Resolution...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Ayazoglu, Mustafa Aselsan Res Ankara Turkey
Single-Image-Super-Resolution (SISR) is a classical computer vision problem that has benefited from the recent advancements in deep learning methods, especially the advancements of convolutional neural networks (CNN).... 详细信息
来源: 评论
Fast building segmentation from satellite imagery and few local labels
Fast building segmentation from satellite imagery and few lo...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Robinson, Caleb Ortiz, Anthony Park, Hogeun Gracia, Nancy Lozano Kaw, Jon Kher Sederholm, Tina Dodhia, Rahul Ferres, Juan M. Lavista Microsoft AI Good Res Lab Redmond WA 98052 USA World Bank 1818 H St NW Washington DC 20433 USA
Innovations in computer vision algorithms for satellite image analysis can enable us to explore global challenges such as urbanization and land use change at the planetary level. However, domain shift problems are a c... 详细信息
来源: 评论
Strengthening the Transferability of Adversarial Examples Using Advanced Looking Ahead and Self-CutMix
Strengthening the Transferability of Adversarial Examples Us...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Jang, Donggon Son, Sanghyeok Kim, Dae-Shik Korea Adv Inst Sci & Technol KAIST Daejeon South Korea
Deep neural networks (DNNs) are vulnerable to adversarial examples generated by adding malicious noise imperceptible to a human. The adversarial examples successfully fool the models under the white-box setting, but t... 详细信息
来源: 评论
LSDIR: A Large Scale Dataset for Image Restoration
LSDIR: A Large Scale Dataset for Image Restoration
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2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023
作者: Li, Yawei Zhang, Kai Liang, Jingyun Cao, Jiezhang Liu, Ce Gong, Rui Zhang, Yulun Tang, Hao Liu, Yun Demandolx, Denis Ranjan, Rakesh Timofte, Radu Van Gool, Luc Computer Vision Lab Eth Zürich Switzerland Meta Reality Labs United States University of Würzburg Germany Ku Leuven Belgium
The aim of this paper is to propose a large scale dataset for image restoration (LSDIR). Recent work in image restoration has been focused on the design of deep neural networks. The datasets used to train these networ... 详细信息
来源: 评论
3D Point Cloud Instance Segmentation of Lettuce Based on PartNet
3D Point Cloud Instance Segmentation of Lettuce Based on Par...
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IEEE/CVF conference on computer vision and pattern recognition (CVPR)
作者: Wang, Luhan Zheng, Lihua Wang, Minjuan China Agr Univ Key Lab Smart Agr Syst Minist Educ Beijing 100083 Peoples R China
Organ level instance segmentation (e.g., individual leaves) based on computer vision techniques is a key step in the measurement of plant phenotypes. Since plant organs, especially leaves, are self-occluded and emerge... 详细信息
来源: 评论