检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

8,902 篇 会议
42 篇 期刊文献
18 册 图书

馆藏范围

8,961 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

4,560 篇 工学
- 4,019 篇 计算机科学与技术...
- 2,178 篇 软件工程
- 1,242 篇 光学工程
- 555 篇 控制科学与工程
- 431 篇 信息与通信工程
- 429 篇 机械工程
- 294 篇 电气工程
- 287 篇 仪器科学与技术
- 179 篇 生物工程
- 159 篇 生物医学工程（可授...
- 119 篇 电子科学与技术（可...
- 61 篇 安全科学与工程
- 58 篇 建筑学
- 58 篇 化学工程与技术
- 52 篇 土木工程
- 49 篇 交通运输工程
- 40 篇 力学（可授工学、理...
2,065 篇 理学
- 1,382 篇 物理学
- 1,198 篇 数学
- 420 篇 统计学（可授理学、...
- 238 篇 生物学
- 54 篇 化学
- 36 篇 系统科学
263 篇 管理学
- 180 篇 图书情报与档案管...
- 89 篇 管理科学与工程(可...
- 47 篇 工商管理
222 篇 医学
- 221 篇 临床医学
- 39 篇 基础医学(可授医学...
205 篇 艺术学
- 205 篇 设计学（可授艺术学...
45 篇 法学
- 43 篇 社会学
21 篇 农学
14 篇 教育学
9 篇 经济学
6 篇 军事学

主题

3,413 篇 computer vision
1,216 篇 pattern recognit...
946 篇 cameras
908 篇 conferences
765 篇 computer science
674 篇 image segmentati...
618 篇 layout
598 篇 training
548 篇 shape
518 篇 robustness
451 篇 feature extracti...
448 篇 humans
445 篇 face recognition
405 篇 computational mo...
402 篇 object detection
365 篇 visualization
356 篇 computer archite...
336 篇 application soft...
304 篇 lighting
258 篇 image reconstruc...

机构

41 篇 microsoft resear...
30 篇 department of co...
25 篇 department of co...
23 篇 institute for co...
22 篇 department of co...
22 篇 school of comput...
20 篇 university of sc...
20 篇 swiss fed inst t...
19 篇 tsinghua univers...
19 篇 institute of com...
18 篇 swiss fed inst t...
17 篇 the robotics ins...
17 篇 carnegie mellon ...
17 篇 computer vision ...
17 篇 department of co...
16 篇 institute of inf...
16 篇 school of comput...
15 篇 school of comput...
15 篇 carnegie mellon ...
14 篇 national laborat...

作者

57 篇 timofte radu
25 篇 huang thomas s.
24 篇 van gool luc
23 篇 s.k. nayar
22 篇 nayar shree k.
22 篇 t. kanade
21 篇 jain anil k.
20 篇 luc van gool
19 篇 t.s. huang
18 篇 xiaoou tang
18 篇 murino vittorio
18 篇 horst bischof
17 篇 a.k. jain
17 篇 t. darrell
16 篇 g. healey
16 篇 bowyer kevin w.
16 篇 bischof horst
15 篇 m.j. black
15 篇 li stan z.
15 篇 m. shah

语言

8,913 篇 英文
40 篇 其他
8 篇 中文
1 篇 土耳其文

检索条件"任意字段=IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops"

共 8962 条记录，以下是1731-1740 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Detecting and Segmenting Adversarial Graphics patterns from Images 18

Detecting and Segmenting Adversarial Graphics Patterns from ...

引用

18th ieee/CVF International conference on computer vision (ICCV)

作者： Qu, Xiangyu Chan, Stanley H. Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9781665401913

Adversarial attacks pose a substantial threat to computer vision system security, but the social media industry constantly faces another form of "adversarial attack" in which the hackers attempt to upload inappropriate images and fool the automated screening systems by adding artificial graphics patterns. In this paper, we formulate the defense against such attacks as an artificial graphics pattern segmentation problem. We evaluate the efficacy of several segmentation algorithms and, based on observation of their performance, propose a new method tailored to this specific problem. Extensive experiments show that the proposed method outperforms the baselines and has a promising generalization capability, which is the most crucial aspect in segmenting artificial graphics patterns.

关键词： Graphics Training Image segmentation computer vision computer hacking Social networking (online) Shape

来源：评论

学校读者我要写书评

暂无评论

End-to-end Model-based Gait recognition using Synchronized Multi-view Pose Constraint 18

End-to-end Model-based Gait Recognition using Synchronized M...

引用

18th ieee/CVF International conference on computer vision (ICCV)

作者： Li, Xiang Makihara, Yasushi Xu, Chi Yagi, Yasushi Osaka Univ Osaka Japan

ISBN: (纸本)9781665401913

We propose an end-to-end model-based cross-view gait recognition which employs pose sequences and shapes extracted by human model fitting. Specifically, we consider a problem setting where gait sequences from single different views are given as a pair to match in a test phase, while asynchronous multi-view gait sequences are given for each subject in a training phase. This work exploits multi-view constraint in the training phase to extract more consistent pose sequences from any views in the test phase, unlike the existing methods do not consider them. For this purpose, given asynchronous multi-view gait sequences, we introduce a phase synchronization step in the training phase so that we can impose pose consistency at each synchronized phase in a temporally up-sampled phase domain. We then train our network by minimizing a loss function based on the synchronized multi-view pose constraint as well as shape consistency, temporal pose smoothness, recognition accuracy, etc in an end-to-end manner. We also introduce the synchronization step in a test phase to reduce intra-subject variations caused by asynchronous pose features. Experimental results on the OU-MVLP and CASIA-B datasets show that the proposed method achieves the state-of-the-art performance for both gait identification and verification scenarios, especially a great improvement in terms of the pose representations.

关键词： Training Legged locomotion computer vision Shape conferences Fitting Synchronization

来源：评论

学校读者我要写书评

暂无评论

Proceedings - 2019 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2019

Proceedings - 2019 IEEE/CVF Conference on Computer Vision an...

引用

32nd ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2019

ISBN: (纸本)9781728125060

The proceedings contain 355 papers. The topics discussed include: MultiNet++: multi-stream feature aggregation and geometric loss strategy for multi-task learning;privacy-preserving action recognition using coded aperture videos;evading face recognition via partial tampering of faces;privacy-preserving annotation of face images through attribute-preserving face synthesis;towards deep neural network training on encrypted data;fooling automated surveillance cameras: adversarial patches to attack person detection;anonymousnet: natural face de-identification with measurable privacy;regularizer to mitigate gradient masking effect during single-step adversarial training;privacy preserving group membership verification and identification;defending against adversarial attacks using random forest;intersection to overpass: instance segmentation on filamentous structures with an orientation-aware neural network and terminus pairing algorithm;and surface parameterization and registration for statistical multiscale atlasing of organ development.

关键词：

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2024 Image Shadow Removal Challenge Report

NTIRE 2024 Image Shadow Removal Challenge Report

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Florin-Alexandru Vasluianu Tim Seizinger Zhuyun Zhou Zongwei Wu Cailian Chen Radu Timofte Wei Dong Han Zhou Yuqiong Tian Jun Chen Xueyang Fu Xin Lu Yurui Zhu Xi Wang Dong Li Jie Xiao Yunpeng Zhang Zheng-Jun Zha Zhao Zhang Suiyi Zhao Bo Wang Yan Luo Yanyan Wei Zhihao Zhao Long Sun Tingting Yang Jinshan Pan Jiangxin Dong Jinhui Tang Bilel Benjdira Mohammed Nassif Anis Koubaa Ahmed Elhayek Anas M. Ali Kyotaro Tokoro Kento Kawai Kaname Yokoyama Takuya Seno Yuki Kondo Norimichi Ukita Chenghua Li Bo Yang Zhiqi Wu Gao Chen Yihan Yu Sixiang Chen Kai Zhang Tian Ye Wenbin Zou Yunlong Lin Zhaohu Xing Jinbin Bai Wenhao Chai Lei Zhu Ritik Maheshwari Rakshank Verma Rahul Tekchandani Praful Hambarde Satya Narayan Tazi Santosh Kumar Vipparthi Subrahmanyam Murala Jaeho Lee Seongwan Kim Sharif S M A Nodirkhuja Khujaev Roman Tsoy Fan Gao Weidan Yan Wenze Shao Dengyin Zhang Bin Chen Siqi Zhang Yanxin Qian Yuanbin Chen Yuanbo Zhou Tong Tong Rongfeng Wei Ruiqi Sun Yue Liu Nikhil Akalwadi Amogh Joshi Sampada Malagi Chaitra Desai Ramesh Ashok Tabib Uma Mudenagudi Ali Murtaza Uswah Khairuddin Ahmad ’Athif Mohd Faudzi Adinath Dukre Vivek Deshmukh Shruti S. Phutke Ashutosh Kulkarni Anil Gonde Arun karthik K Manasa N Shri Hari Priya Wei Hao Xingzhuo Yan Minghan Fu

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

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 reconstruction, and a separate ranking for high performing perceptual quality solutions. Track 1 (fidelity) had 214 registered participants, with 17 teams submitting in the final phase, while Track 2 (perceptual) registered 185 participants, resulting in 18 final phase submissions. Both tracks were based on data from the WSRD dataset, simulating interactions between self-shadows and cast shadows, with a large variety of represented objects, textures, and materials. Improved image alignment enabled increased fidelity reconstruction, with restored frames mostly indistinguishable from the references images for top performing solutions.

关键词： computer vision Reviews Image synthesis conferences Buildings Image restoration pattern recognition

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2024 Challenge on Low Light Image Enhancement: Methods and Results

NTIRE 2024 Challenge on Low Light Image Enhancement: Methods...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xiaoning Liu Zongwei Wu Ao Li Florin-Alexandru Vasluianu Yulun Zhang Shuhang Gu Le Zhang Ce Zhu Radu Timofte Zhi Jin Hongjun Wu Chenxi Wang Haitao Ling Yuanhao Cai Hao Bian Yuxin Zheng Jing Lin Alan Yuille Ben Shao Jin Guo Tianli Liu Mohao Wu Yixu Feng Shuo Hou Haotian Lin Yu Zhu Peng Wu Wei Dong Jinqiu Sun Yanning Zhang Qingsen Yan Wenbin Zou Weipeng Yang Yunxiang Li Qiaomu Wei Tian Ye Sixiang Chen Zhao Zhang Suiyi Zhao Bo Wang Yan Luo Zhichao Zuo Mingshen Wang Junhu Wang Yanyan Wei Xiaopeng Sun Yu Gao Jiancheng Huang Hongming Chen Xiang Chen Hui Tang Yuanbin Chen Yuanbo Zhou Xinwei Dai Xintao Qiu Wei Deng Qinquan Gao Tong Tong Mingjia Li Jin Hu Xinyu He Xiaojie Guo Sabarinathan Sabarinathan K Uma A Sasithradevi B Sathya Bama S. Mohamed Mansoor Roomi V. Srivatsav Jinjuan Wang Long Sun Qiuying Chen Jiahong Shao Yizhi Zhang Marcos V. Conde Daniel Feijoo Juan C. Benito Alvaro García Jaeho Lee Seongwan Kim Sharif S M A Nodirkhuja Khujaev Roman Tsoy Ali Murtaza Uswah Khairuddin Ahmad ’Athif Mohd Faudzi Sampada Malagi Amogh Joshi Nikhil Akalwadi Chaitra Desai Ramesh Ashok Tabib Uma Mudenagudi Wenyi Lian Wenjing Lian Jagadeesh Kalyanshetti Vijayalaxmi Ashok Aralikatti Palani Yashaswini Nitish Upasi Dikshit Hegde Ujwala Patil Sujata C Xingzhuo Yan Wei Hao Minghan Fu Pooja Choksy Anjali Sarvaiya Kishor Upla Kiran Raja Hailong Yan Yunkai Zhang Baiang Li Jingyi Zhang Huan Zheng

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the NTIRE 2024 low light image enhancement challenge, highlighting the proposed solutions and results. The aim of this challenge is to discover an effective network design or solution capable of generating brighter, clearer, and visually appealing results when dealing with a variety of conditions, including ultra-high resolution (4K and beyond), non-uniform illumination, backlighting, extreme darkness, and night scenes. A notable total of 428 participants registered for the challenge, with 22 teams ultimately making valid submissions. This paper meticulously evaluates the state-of-the-art advancements in enhancing low-light images, reflecting the significant progress and creativity in this field.

关键词： computer vision Image resolution Reviews conferences Lighting pattern recognition Image enhancement

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2021 NonHomogeneous Dehazing Challenge Report

NTIRE 2021 NonHomogeneous Dehazing Challenge Report

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ancuti, Codruta O. Ancuti, Cosmin Vasluianu, Florin-Alexandru Timofte, Radu Fu, Minghan Liu, Huan Yu, Yankun Chen, Jun Wang, Keyan Chang, Jerome Wang, Xiyao Liu, Jing Xu, Yi Zhang, Xinjian Zhao, Minyi Zhou, Shuigeng Chen, Tianyi Fu, Jiahui Jiang, Wentao Gao, Chen Liu, Si Wang, Yudong Guo, Jichang Li, Chongyi Yan, Qixin Zheng, Sida Zamir, Syed Waqas Arora, Aditya Dudhane, Akshay Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Shao, Ling Zhang, Haichuan Guo, Tiantong Monga, Vishal Yang, Wenjin Lin, Jin Luo, Xiaotong Huang, Guowen Chen, Shuxin Qu, Yanyun Xu, Kele Yang, Lehan Sun, Pengliang Niu, Xuetong Zheng, Junjun Ruan, Xiaotong Wang, Yunfeng Yang, Jiang Luo, Zhipeng Wang, Sai Xu, Zhenyu Cao, Xiaochun Luo, Jun Zheng, Zhuoran Ren, Wenqi Wang, Tao Chen, Yiqun Leng, Cong Li, Chenghua Cheng, Jian Sung, Chang-Sung Chen, Jun-Cheng Jo, Eunsung Sim, Jae-Young Geethu, M. M. Akhil, K. A. Sreeni, K. G. Jeena, R. S. Zacharias, Joseph Manu, Chippy M. Huang, Zexi Zhang, Baofeng Zhang, Yiwen Li, Jindong Chen, Mianjie Xiao, Quan Su, Qingchao Han, Lihua Huang, Yanting Prajapati, Kalpesh Chudasama, Vishal Patel, Heena Sarvaiya, Anjali Upla, Kishor Raja, Kiran Ramachandra, Raghavendra Busch, Christoph Jing, Hongyuan Huang, Zilong Fu, Yiran Wu, Haoqiang Zha, Quanxing Zhu, Zhiwei Lv, Hejun Univ Politehn Timisoara ETcTI Timisoara Romania UCL ICTEAM Louvain Belgium Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland McMaster Univ Hamilton ON Canada Xidian Univ Xian Peoples R China Natl Taiwan Univ Taipei Taiwan Univ Chinese Acad Sci Inst Automat Ctr Res Intelligent Syst & Engn Beijing Peoples R China Bilibili Inc Shanghai Peoples R China Fudan Univ Shanghai Peoples R China Beihang Univ Beijing Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Nanyang Technol Univ NTU Sch Comp Sci & Engn Singapore Singapore Incept Inst Artificial Intelligence IIAI Abu Dhabi U Arab Emirates Penn State Univ Sch Elect Engn & Comp Sci Informat Proc & Algorithms Lab iPAL University Pk PA 16802 USA Xiamen Univ Dept Comp Sci Xiamen Peoples R China Key Lab Parallel & Distributed Proc Changsha Peoples R China Alibaba Inc Shanghai Peoples R China DeepBlue Technol Shanghai Co Ltd Shanghai Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing Peoples R China CASIA Beijing Peoples R China CASIA AiRiA Beijing Peoples R China Natl Taiwan Univ Data Sci Degree Program Taipei Taiwan Ulsan Natl Inst Sci & Technol Ulsan South Korea Coll Engn Dept ECE CV Lab Trivandrum Kerala India AICTE Univ Kollam Kerala India South China Univ Techonol Sch Elect & Informat Engn Guangzhou Peoples R China Shenzhen Wave Kingdom Co Ltd Shenzhen Peoples R China Sardar Vallabhbhai Natl Inst Technol Surat India Beijing Union Univ Coll Robot Beijing Peoples R China

ISBN: (纸本)9781665448994

This work reviews the results of the NTIRE 2021 Challenge on Non-Homogeneous Dehazing. The proposed techniques and their results have been evaluated on a novel dataset that extends the NH-Haze datset. It consists of additional 35 pairs of real haze free and nonhomogeneous hazy images recorded outdoor. The nonhomogeneous haze has been introduced in the outdoor scenes by using a a professional setup that imitates the real conditions of haze scenes. 327 participants registered in the challenge and 23 teams competed in the final testing phase. The proposed solutions gauge the state-of-the-art in image dehazing.

关键词： computer vision Correlation conferences Architecture computer architecture pattern recognition Testing

来源：评论

学校读者我要写书评

暂无评论

BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification

BiCnet-TKS: Learning Efficient Spatial-Temporal Representati...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hou, Ruibing Chang, Hong Ma, Bingpeng Huang, Rui Shan, Shiguang Chinese Acad Sci Inst Comp Technol CAS Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Univ Hong Kong Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Guangdong Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai 200031 Peoples R China

ISBN: (纸本)9781665445092

In this paper, we present an efficient spatial-temporal representation for video person re-identification (reID). Firstly, we propose a Bilateral Complementary Network (BiCnet) for spatial complementarity modeling. Specifically, BiCnet contains two branches. Detail Branch processes frames at original resolution to preserve the detailed visual clues, and Context Branch with a down-sampling strategy is employed to capture long-range contexts. On each branch, BiCnet appends multiple parallel and diverse attention modules to discover divergent body parts for consecutive frames, so as to obtain an integral characteristic of target identity. Furthermore, a Temporal Kernel Selection (TKS) block is designed to capture short-term as well as long-term temporal relations by an adaptive mode. TKS can be inserted into BiCnet at any depth to construct BiCnet-TKS for spatial-temporal modeling. Experimental results on multiple benchmarks show that BiCnet-TKS outperforms state-of-the-arts with about 50% less computations.

关键词： Visualization computer vision Codes Computational modeling Benchmark testing pattern recognition Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey

Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Marcos V. Conde Zhijun Lei Wen Li Ioannis Katsavounidis Radu Timofte Min Yan Xin Liu Qian Wang Xiaoqian Ye Zhan Du Tiansen Zhang Zhiyuan Li Hao Wei Chenyang Ge Jiangtao Lv Long Sun Jinshan Pan Jiangxin Dong Jinhui Tang Menghan Zhou Yiqiang Yan Kihwan Yoon Ganzorig Gankhuyag Jae-Hyeon Lee Ui-Jin Choi Hyeon-Cheol Moon Tae-Hyun Jeong Yoonmo Yang Jae-Gon Kim Jinwoo Jeong Sunjei Kim Xintao Qiu Yuanbo Zhou Kongxian Wu Xinwei Dai Hui Tang Wei Deng Qingquan Gao Tong Tong Long Peng Jiaming Guo Xin Di Bohao Liao Zhibo Du Peize Xia Renjing Pei Yang Wang Yang Cao Zhengjun Zha Bingnan Han Hongyuan Yu Zhuoyuan Wu Cheng Wan Yuqing Liu Haodong Yu Jizhe Li Zhijuan Huang Yuan Huang Yajun Zou Xianyu Guan Qi Jia Heng Zhang Xuanwu Yin Kunlong Zuo Dongyang Zhang Tianle Liu Huaian Chen Yi Jin CAIDAS & IFI Computer Vision Lab. University of Wurzbürg Meta

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a novel benchmark for efficient image upscaling as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to up scale compressed images from 540p to native 4K resolution (4x factor) in real-time on commercial GPUs. For this, we use a diverse test set containing diverse 4K images ranging from digital art to gaming and photography. The images are compressed using the modern AVIF codec, instead of JPEG. All the proposed methods improve PSNR fidelity over Lanczos interpolation, and process images under 30ms. Out of the 160 participants, 25 teams submitted their code. This survey considers only the most novel solutions models, making it the most comprehensive benchmark on real-time SR of compressed images using modern codecs.

关键词： Surveys Photography Image coding Codecs Superresolution Transform coding Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 ieee.

关键词： Optical resolving power

来源：评论

学校读者我要写书评

暂无评论

Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark

Sewer-ML: A Multi-Label Sewer Defect Classification Dataset ...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Haurum, Joakim Bruslund Moeslund, Thomas B. Aalborg Univ Visual Anal & Percept VAP Lab Aalborg Denmark

ISBN: (纸本)9781665445092

Perhaps surprisingly sewerage infrastructure is one of the most costly infrastructures in modern society. Sewer pipes are manually inspected to determine whether the pipes are defective. However, this process is limited by the number of qualified inspectors and the time it takes to inspect a pipe. Automatization of this process is therefore of high interest. So far, the success of computer vision approaches for sewer defect classification has been limited when compared to the success in other fields mainly due to the lack of public datasets. To this end, in this work we present a large novel and publicly available multi-label classification dataset for image-based sewer defect classification called Sewer-ML. The Sewer-ML dataset consists of 1.3 million images annotated by professional sewer inspectors from three different utility companies across nine years. Together with the dataset, we also present a benchmark algorithm and a novel metric for assessing performance. The benchmark algorithm is a result of evaluating 12 state-of-the-art algorithms, six from the sewer defect classification domain and six from the multi-label classification domain, and combining the best performing algorithms. The novel metric is a class-importance weighted F2 score, F2CIW, reflecting the economic impact of each class, used together with the normal pipe F1 score, F1Normal. The benchmark algorithm achieves an F2CIW score of 55.11% and F1Normal score of 90.94%, leaving ample room for improvement on the SewerML dataset. The code, models, and dataset are available at the project page http://***/sewer-ml

关键词： Measurement Economics computer vision Codes Biological system modeling Benchmark testing Inspection

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共500页 << < 170 171 172 173 174 175 176 177 178 179 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：