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检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"
6538 条 记 录,以下是191-200 订阅
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Variational Autoencoders for Generating Hyperspectral Imaging Honey Adulteration Data
Variational Autoencoders for Generating Hyperspectral Imagin...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Phillips, Tessa Abdulla, Waleed Univ Auckland Auckland New Zealand
Honey fraud and adulteration are an increasing concern globally. Hyperspectral imaging and machine learning can detect adulterated honey within a known set of honey, where we have captured data at different sugar conc... 详细信息
来源: 评论
User-Guided Variable Rate Learned Image Compression
User-Guided Variable Rate Learned Image Compression
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Gupta, Rushil Suryateja, B., V Kapoor, Nikhil Jaiswal, Rajat Nangi, Sharmila Kulkarni, Kuldeep Adobe Res Bengaluru India Indian Inst Technol Delhi Delhi India Stanford Univ Stanford CA 94305 USA
We propose a learning-based image compression method that achieves any arbitrary input bitrate via user-guided bit allocation to preferred regions. We verify our hypothesis of incorporating user guidance for bitrate c... 详细信息
来源: 评论
NTIRE 2024 Image Shadow Removal Challenge Report
NTIRE 2024 Image Shadow Removal Challenge Report
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Vasluianu, Florin-Alexandru Seizinger, Tim Wu, Zongwei Zhou, Zhuyun Chen, Cailian Zhou, Han Timofte, Radu Dong, Wei Tian, Yuqiong Chen, Jun Lul, Xin Zhu, Yurui Wang, Xi Li, Dong Xiao, Jie Zhang, Yunpeng Fu, Xueyang Zha, Zheng-Jun Zhang, Zhao Zhao, Suiyi Wang, Bo Luo, Yan Wei, Yanyan Xiaol, Jie Ful, Xueyang Zhal, Zheng-Jun Lu, Xin Zhao, Zhihao Sun, Long Yang, Tingting Pan, Jinshan Tang, Jinhui Dong, Jiangxin Benjdira, Bilel Nassif, Mohammed Koubaa, Anis Elhayek, Ahmed Ali, Anas M. Tokoro, Kyotaro Kawai, Kento Yokoyama, Kaname Seno, Takuya Kondo, Yuki Ukita, Norimichi Li, Chenghua Yang, Bo Wu, Zhiqi Chen, Gao Yu, Yihan Chen, Sixiang Mane, Kai Ye, Tian Zou, Wenbin Lin, Yunlong Xing, Zhaohu Bai, Jinbin Chai, Wenhao Zhu, Lei Maheshwari, Ritik Verma, Rakshank Tekchandanil, Rahul Hambarde, Praful Tazil, Satya Narayan Vipparthi, Santosh Kumar Murala, Subrahmanyam Lee, Jaeho Kim, Seongwan Sharif, S. M. A. Khujaev, Nodirkhuja Tsoy, Roman Gao, Fan Yan, Weidan Shao, Wenze Zhang, Dengyin Chen, Bin Zhang, Siqi Qian, Yanxin Chen, Yuanbin Zhou, Yuanbo Tong, Tong Wei, Rongfeng Sun, Ruiqi Liu, Yue Akalwadi, Nikhil Joshi, Amogh Malagi, Sampada Desai, Chaitra Tabib, Ramesh Ashok Mudenagudi, Uma Murtaza, Ali Khairuddin, Uswah Faudzi, Ahmad'Athif Mohd Dukre, Adinath Deshmukh, Vivek Phutke, Shruti S. Kulkarni, Ashutosh Gonde, Anil Karthik, Arun K. Manasa, N. Priyal, Shri Hari Hao, Wei Yan, Xingzhuo Fu, Minghan Univ Wurzburg Comp Vis Lab IFI & CAIDAS Wurzburg Germany Shanghai Jiao Tong Univ Shanghai Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada Univ Sci & Technol China Hefei Peoples R China Hefei Univ Technol Hefei Peoples R China Nanjing Univ Sci & Technol Nanjing Jiangsu Peoples R China Prince Sultan Univ Robot & Internet Things Lab Riyadh 12435 Saudi Arabia Prince Muqrin Univ Artificial Intelligence Dept Medinah 41311 Saudi Arabia Toyota Technol Inst Intelligent Informat Media Lab Nagoya Japan Nanjing Artificial Intelligence Res IA AiRiA Nanjing Peoples R China Nanjing Normal Univ High Sch Jiangning Campus Nanjing Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China South China Univ Technol Guangzhou Peoples R China Xiamen Univ Xiamen Peoples R China Natl Univ Singapore Singapore Singapore Univ Washington Seattle WA 98195 USA GEC Ajmer Kiranipura India CVPR Lab IIT Ropar Rupnagar India SCSS Trinity Coll Dublin Dublin Ireland Opt AI Seoul South Korea Nanjing Univ Posts & Telecommun Nanjing Peoples R China Fuzhou Univ Fuzhou Peoples R China Univ Hong Kong Logist & Supply Chain MultiTech R&D Ctr Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China KLE Technol Univ Ctr Excellence Visual Intelligence CEVI Hubballi Karnataka India KLE Technol Univ Sch Elect & Commun Engn Hubballi Karnataka India KLE Technol Univ Sch Comp Sci & Engn Hubballi Karnataka India Univ Teknol Malaysia Malaysia Japan Int Inst Technol MMT Kuala Lumpur Malaysia Univ Teknol Malaysia Ctr Artificial Intelligence & Robot CAIRO Kuala Lumpur Malaysia Shri Guru Gobind Singhji Inst Engn & Technol Nanded India Indian Inst Technol Ropar Comp Vis & Pattern Recognit Lab Rupnagar India Trinity Coll Dublin Sch Comp Sci & Stat CVPR Lab Dublin Ireland Shiv Nadar Univ Sch Engn Chennai Tamil Nadu India Fortinet Inc Sunnyvale CA USA Bosch Investment Ltd Shanghai Peoples R China Univ Saskatchewan Saskatoon
This work reviews the results of the NTIRE 2024 Challenge on Shadow Removal. Building on the last year edition, the current challenge was organized in two tracks, with a track focused on increased fidelity reconstruct... 详细信息
来源: 评论
MV-TAL: Mulit-view Temporal Action Localization in Naturalistic Driving
MV-TAL: Mulit-view Temporal Action Localization in Naturalis...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Li, Wei Chen, Shimin Gu, Jianyang Wang, Ning Chen, Chen Guo, Yandong OPPO Res Inst Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China East China Univ Sci & Technol Shanghai Peoples R China
Human risky behavior in driving is an important visual recognition problem. In this paper, we propose a multi-view temporal action localization system based on the grayscale video to achieve action recognition in natu... 详细信息
来源: 评论
SMM-Conv: Scalar Matrix Multiplication with Zero Packing for Accelerated Convolution
SMM-Conv: Scalar Matrix Multiplication with Zero Packing for...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Ofir, Amir Ben-Artzi, Gil Ariel Univ Ariel Israel
We present a novel approach for accelerating convolutions during inference for CPU-based architectures. The most common method of computation involves packing the image into the columns of a matrix (im2col) and perfor... 详细信息
来源: 评论
Towards Detailed Characteristic-Preserving Virtual Try-On
Towards Detailed Characteristic-Preserving Virtual Try-On
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Lee, Sangho Lee, Seoyoung Lee, Joonseok Seoul Natl Univ Seoul South Korea
While virtual try-on has rapidly progressed recently, existing virtual try-on methods still struggle to faithfully represent various details of the clothes when worn. In this paper, we propose a simple yet effective m... 详细信息
来源: 评论
PaintInStyle: One-Shot Discovery of Interpretable Directions by Painting
PaintInStyle: One-Shot Discovery of Interpretable Directions...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Doner, Berkay Balcioglu, Elif Sema Barin, Merve Rabia Kocasari, Umut Tiftikci, Mert Yanardag, Pinar Bogazici Univ Istanbul Turkey
The search for interpretable directions in latent spaces of pre-trained Generative Adversarial Networks (GANs) has become a topic of interest. These directions can be utilized to perform semantic manipulations on the ... 详细信息
来源: 评论
Area Under the ROC Curve Maximization for Metric Learning
Area Under the ROC Curve Maximization for Metric Learning
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Gajic, Bojana Amato, Ariel Baldrich, Ramon van de Weijer, Joost Gatta, Carlo Vintra Inc Barcelona Spain Comp Vis Ctr Barcelona Spain
Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing t... 详细信息
来源: 评论
Proposal-free Lidar Panoptic Segmentation with Pillar-level Affinity
Proposal-free Lidar Panoptic Segmentation with Pillar-level ...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Chen, Qi Vora, Sourabh Johns Hopkins Univ Baltimore MD 21218 USA Motional Boston MA USA
We propose a simple yet effective proposal-free architecture for lidar panoptic segmentation. We jointly optimize both semantic segmentation and class-agnostic instance classification in a single network using a pilla... 详细信息
来源: 评论
SCVRL: Shuffled Contrastive Video Representation Learning
SCVRL: Shuffled Contrastive Video Representation Learning
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Dorkenwald, Michael Xiao, Fanyi Brattoli, Biagio Tighe, Joseph Modolo, Davide Heidelberg Univ Heidelberg Germany AWS AI Labs Palo Alto CA USA AWS Palo Alto CA USA
We propose SCVRL, a novel contrastive-based framework for self-supervised learning for videos. Differently from previous contrast learning based methods that mostly focus on learning visual semantics (e.g., CVRL), SCV... 详细信息
来源: 评论