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

3,317 篇 会议
3 篇 期刊文献

馆藏范围

3,320 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,915 篇 工学
- 1,820 篇 计算机科学与技术...
- 376 篇 软件工程
- 142 篇 机械工程
- 136 篇 光学工程
- 42 篇 生物工程
- 28 篇 信息与通信工程
- 11 篇 控制科学与工程
- 9 篇 电气工程
- 9 篇 电子科学与技术（可...
- 9 篇 化学工程与技术
- 9 篇 交通运输工程
- 8 篇 生物医学工程（可授...
- 7 篇 安全科学与工程
- 4 篇 材料科学与工程（可...
- 4 篇 建筑学
- 3 篇 土木工程
- 3 篇 农业工程
174 篇 理学
- 136 篇 物理学
- 43 篇 生物学
- 29 篇 数学
- 16 篇 统计学（可授理学、...
- 10 篇 化学
29 篇 医学
- 28 篇 临床医学
- 3 篇 基础医学(可授医学...
15 篇 管理学
- 8 篇 管理科学与工程(可...
- 7 篇 图书情报与档案管...
- 3 篇 工商管理
5 篇 法学
- 3 篇 社会学
- 2 篇 法学
2 篇 教育学
- 2 篇 教育学
2 篇 农学
1 篇 经济学

主题

1,179 篇 computer vision
801 篇 conferences
570 篇 training
484 篇 pattern recognit...
330 篇 computational mo...
279 篇 computer archite...
255 篇 visualization
180 篇 feature extracti...
160 篇 neural networks
153 篇 semantics
152 篇 task analysis
145 篇 cameras
143 篇 deep learning
140 篇 three-dimensiona...
139 篇 benchmark testin...
126 篇 low-level vision
117 篇 vision
115 篇 language
113 篇 image segmentati...
112 篇 estimation

机构

35 篇 tsinghua univ pe...
34 篇 univ sci & techn...
28 篇 shanghai ai lab ...
26 篇 chinese univ hon...
25 篇 university of sc...
25 篇 tsinghua univers...
24 篇 peng cheng labor...
23 篇 swiss fed inst t...
23 篇 swiss fed inst t...
23 篇 peng cheng lab p...
22 篇 zhejiang univ pe...
22 篇 carnegie mellon ...
20 篇 univ chinese aca...
20 篇 university of ch...
18 篇 harbin inst tech...
18 篇 shanghai jiao to...
18 篇 sensetime res pe...
16 篇 hong kong univ s...
15 篇 fudan univ peopl...
14 篇 national key lab...

作者

60 篇 timofte radu
26 篇 radu timofte
19 篇 van gool luc
16 篇 lei lei
15 篇 loy chen change
15 篇 qiao yu
14 篇 fan haoqiang
13 篇 zhang yulun
13 篇 liu shuaicheng
13 篇 luc van gool
12 篇 chen chen
12 篇 chen wei-ting
12 篇 li chongyi
11 篇 wangmeng zuo
11 篇 pan jinshan
11 篇 marcos v. conde
10 篇 wei dong
10 篇 zhou shangchen
10 篇 suiyi zhao
10 篇 yu lei

语言

3,319 篇 英文
1 篇 其他

检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是3311-3320 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：

VINECS: Video-based Neural Character Skinning

VINECS: Video-based Neural Character Skinning

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zhouyingcheng Liao Vladislav Golyanik Marc Habermann Christian Theobalt Max Planck Institute for Informatics Saarland Informatics Campus The University of Hong Kong Saarbrücken Research Center for Visual Computing Interaction and AI

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Rigging and skinning clothed human avatars is a challenging task and traditionally requires a lot of manual work and expertise. Recent methods addressing it either generalize across different characters or focus on capturing the dynamics of a single character observed under different pose configurations. However, the former methods typically predict solely static skinning weights, which perform poorly for highly articulated poses, and the latter ones either require dense 3D character scans in different poses or cannot generate an explicit mesh with vertex correspondence over time. To address these challenges, we propose a fully automated approach for creating a fully rigged character with pose-dependent skinning weights, which can be solely learned from multi-view video. Therefore, we first acquire a rigged template, which is then statically skinned. Next, a coordinate-based MLP learns a skinning weights field parameterized over the position in a canonical pose space and the respective pose. Moreover, we introduce our pose- and view-dependent appearance field allowing us to differentiably render and supervise the posed mesh using multi-view imagery. We show that our approach outperforms state-of-the-art while not relying on dense 4D scans. More details can be found on our project page 1 1 https://***/~mhaberma/projects/2023-Vinecs.

关键词： Geometry Surface reconstruction computer vision Three-dimensional displays Computational modeling Manuals Rendering (computer graphics)

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Eduardo Pé rez-Pellitero Sibi Catley-Chandar Richard Shaw Aleš Leonardis Radu Timofte Zexin Zhang Cen Liu Yunbo Peng Yue Lin Gaocheng Yu Jin Zhang Zhe Ma Hongbin Wang Xiangyu Chen Xintao Wang Haiwei Wu Lin Liu Chao Dong Jiantao Zhou Qingsen Yan Song Zhang Weiye Chen Yuhang Liu Zhen Zhang Yanning Zhang Javen Qinfeng Shi Dong Gong Dan Zhu Mengdi Sun Guannan Chen Yang Hu Haowei Li Baozhu Zou Zhen Liu Wenjie Lin Ting Jiang Chengzhi Jiang Xinpeng Li Mingyan Han Haoqiang Fan Jian Sun Shuaicheng Liu Juan Marí n-Vega Michael Sloth Peter Schneider-Kamp Richard Rö ttger Chunyang Li Long Bao Gang He Ziyao Xu Li Xu Gen Zhan Ming Sun Xing Wen Junlin Li Jinjing Li Chenghua Li Ruipeng Gang Fangya Li Chenming Liu Shuang Feng Fei Lei Rui Liu Junxiang Ruan Tianhong Dai Wei Li Zhan Lu Hengyan Liu Peian Huang Guangyu Ren Yonglin Luo Chang Liu Qiang Tu Sai Ma Yizhen Cao Steven Tel Barthelemy Heyrman Dominique Ginhac Chul Lee Gahyeon Kim Seonghyun Park An Gia Vien Truong Thanh Nhat Mai Howoon Yoon Tu Vo Alexander Holston Sheir Zaheer Chan Y. Park Huawei Noah&#x2019 s Ark Laboratory ETH Z&#x00FC rich University of W&#x00FC rzburg

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022. This manuscript focuses on the competition set-up, datasets, the proposed methods and their results. The challenge aims at estimating an HDR image from multiple respective low dynamic range (LDR) observations, which might suffer from under-or over-exposed regions and different sources of noise. The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i.e. solutions can not exceed a given number of operations). In Track 2, participants are asked to minimize the complexity of their solutions while imposing a constraint on fidelity scores (i.e. solutions are required to obtain a higher fidelity score than the prescribed baseline). Both tracks use the same data and metrics: Fidelity is measured by means of PSNR with respect to a ground-truth HDR image (computed both directly and with a canonical tonemapping operation), while complexity metrics include the number of Multiply-Accumulate (MAC) operations and runtime (in seconds).

关键词： computer vision Runtime conferences Imaging Dynamic range Particle measurements Market research

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

SmartRefine: A Scenario-Adaptive Refinement Framework for Efficient Motion Prediction

SmartRefine: A Scenario-Adaptive Refinement Framework for Ef...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yang Zhou Hao Shao Letian Wang Steven L. Waslander Hongsheng Li Yu Liu SenseTime Research CUHKMMLab University of Toronto CPII under InnoHK Shanghai Artificial Intelligence Laboratory

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Predicting the future motion of surrounding agents is essential for autonomous vehicles (AVs) to operate safely in dy-namic, human-robot-mixed environments. Context information, such as road maps and surrounding agents' states, provides crucial geometric and semantic information for motion behavior prediction. To this end, recent works explore two-stage prediction frameworks where coarse trajectories are first proposed, and then used to select critical context information for trajectory refinement. However, they either incur a large amount of computation or bring limited improvement, if not both. In this paper, we introduce a novel scenario-adaptive refinement strategy, named SmartRefine, to refine prediction with minimal additional computation. Specifically, SmartRefine can comprehensively adapt refinement configurations based on each scenario's properties, and smartly chooses the number of refinement iterations by introducing a quality score to measure the prediction quality and remaining refinement potential of each scenario. SmartRefine is designed as a generic and flexible approach that can be seamlessly integrated into most state-of-the-art motion prediction models. Experiments on Argoverse (1 & 2) show that our method consistently improves the prediction accuracy of multiple state-of-the-art prediction models. Specifically, by adding SmartRefine to QCNet, we outper-form all published ensemble-free works on the Argoverse 2 leaderboard (single agent track) at submission 1 1 November 2023.: Compre-hensive studies are also conducted to ablate design choices and explore the mechanism behind multi-iteration refinement. Codes are available at our webpage.

关键词： Adaptation models computer vision Accuracy Tracking Roads Semantics Predictive models

来源：评论

学校读者我要写书评

暂无评论

DragDiffusion: Harnessing Diffusion Models for Interactive Point-Based Image Editing

DragDiffusion: Harnessing Diffusion Models for Interactive P...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yujun Shi Chuhui Xue Jun Hao Liew Jiachun Pan Hanshu Yan Wenqing Zhang Vincent Y. F. Tan Song Bai National University of Singapore ByteDance Inc

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Accurate and controllable image editing is a challenging task that has attracted significant attention recently. Notably, DRAGGAN developed by Pan et al. (2023) [33] is an interactive point-based image editing framework that achieves impressive editing results with pixel-level precision. However, due to its reliance on generative adversarial networks (GANs), its generality is limited by the capacity of pretrained GAN models. In this work, we extend this editing framework to diffusion models and propose a novel approach Dragdiffusion. By harnessing large-scale pretrained diffusion models, we greatly enhance the applicability of interactive point-based editing on both real and diffusion-generated images. Unlike other diffusion-based editing methods that provide guidance on diffusion latents of multiple time steps, our approach achieves efficient yet accurate spatial control by optimizing the latent of only one time step. This novel design is motivated by our observations that UNet features at a specific time step provides sufficient semantic and geometric information to support the drag-based editing. Moreover, we introduce two additional techniques, namely identity-preserving fine-tuning and reference-latent-control, to further preserve the identity of the original image. Lastly, we present a challenging benchmark dataset called DRAGBENCH─ the first benchmark to evaluate the performance of interactive point-based image editing methods. Experiments across a wide range of challenging cases (e.g., images with multiple objects, diverse object categories, various styles, etc.) demonstrate the versatility and generality of Dragdiffusion. Code and the Dragbench dataset: https://***/Yujun-Shi/DragDiffusion.

关键词： computer vision Accuracy Codes Semantics Benchmark testing Diffusion models Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

NTIRE 2024 Quality Assessment of AI-Generated Content Challe...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xiaohong Liu Xiongkuo Min Guangtao Zhai Chunyi Li Tengchuan Kou Wei Sun Haoning Wu Yixuan Gao Yuqin Cao Zicheng Zhang Xiele Wu Radu Timofte Fei Peng Huiyuan Fu Anlong Ming Chuanming Wang Huadong Ma Shuai He Zifei Dou Shu Chen Huacong Zhang Haiyi Xie Chengwei Wang Baoying Chen Jishen Zeng Jianquan Yang Weigang Wang Xi Fang Xiaoxin Lv Jun Yan Tianwu Zhi Yabin Zhang Yaohui Li Yang Li Jingwen Xu Jianzhao Liu Yiting Liao Junlin Li Zihao Yu Fengbin Guan Yiting Lu Xin Li Hossein Motamednia S. Farhad Hosseini-Benvidi Ahmad Mahmoudi-Aznaveh Azadeh Mansouri Ganzorig Gankhuyag Kihwan Yoon Yifang Xu Haotian Fan Fangyuan Kong Shiling Zhao Weifeng Dong Haibing Yin Li Zhu Zhiling Wang Bingchen Huang Avinab Saha Sandeep Mishra Shashank Gupta Rajesh Sureddi Oindrila Saha Luigi Celona Simone Bianco Paolo Napoletano Raimondo Schettini Junfeng Yang Jing Fu Wei Zhang Wenzhi Cao Limei Liu Han Peng Weijun Yuan Zhan Li Yihang Cheng Yifan Deng Haohui Li Bowen Qu Yao Li Shuqing Luo Shunzhou Wang Wei Gao Zihao Lu Marcos V. Conde Xinrui Wang Zhibo Chen Ruling Liao Yan Ye Qiulin Wang Bing Li Zhaokun Zhou Miao Geng Rui Chen Xin Tao Xiaoyu Liang Shangkun Sun Xingyuan Ma Jiaze Li Mengduo Yang Haoran Xu Jie Zhou Shiding Zhu Bohan Yu Pengfei Chen Xinrui Xu Jiabin Shen Zhichao Duan Erfan Asadi Jiahe Liu Qi Yan Youran Qu Xiaohui Zeng Lele Wang Renjie Liao

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact *** video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC.

关键词： Image quality computer vision conferences Computational modeling Market research Quality assessment pattern recognition

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yawei Li Kai Zhang Radu Timofte Luc Van Gool Fangyuan Kong Mingxi Li Songwei Liu Zongcai Du Ding Liu Chenhui Zhou Jingyi Chen Qingrui Han Zheyuan Li Yingqi Liu Xiangyu Chen Haoming Cai Yu Qiao Chao Dong Long Sun Jinshan Pan Yi Zhu Zhikai Zong Xiaoxiao Liu Zheng Hui Tao Yang Peiran Ren Xuansong Xie Xian-Sheng Hua Yanbo Wang Xiaozhong Ji Chuming Lin Donghao Luo Ying Tai Chengjie Wang Zhizhong Zhang Yuan Xie Shen Cheng Ziwei Luo Lei Yu Zhihong Wen Qi Wul Youwei Li Haoqiang Fan Jian Sun Shuaicheng Liu Yuanfei Huang Meiguang Jin Hua Huang Jing Liu Xinjian Zhang Yan Wang Lingshun Long Gen Li Yuanfan Zhang Zuowei Cao Lei Sun Panaetov Alexander Yucong Wang Minjie Cai Li Wang Lu Tian Zheyuan Wang Hongbing Ma Jie Liu Chao Chen Yidong Cai Jie Tang Gangshan Wu Weiran Wang Shirui Huang Honglei Lu Huan Liu Keyan Wang Jun Chen Shi Chen Yuchun Miao Zimo Huang Lefei Zhang Mustafa Ayazoğ lu Wei Xiong Chengyi Xiong Fei Wang Hao Li Ruimian Wen Zhijing Yang Wenbin Zou Weixin Zheng Tian Ye Yuncheng Zhang Xiangzhen Kong Aditya Arora Syed Waqas Zamir Salman Khan Munawar Hayat Fahad Shahbaz Khan Dandan Gao Dengwen Zhou Qian Ning Jingzhu Tang Han Huang Yufei Wang Zhangheng Peng Haobo Li Wenxue Guan Shenghua Gong Xin Li Jun Liu Wanjun Wang Kun Zeng Hanjiang Lin Xinyu Chen Jinsheng Fang Computer Vision Lab ETH Zurich

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution.

关键词： Measurement Runtime Convolution conferences Superresolution Memory management Graphics processing units

来源：评论

学校读者我要写书评

暂无评论

Why is the winner the best?

Why is the winner the best?

引用

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

作者： Eisenmann, M. Reinke, A. Weru, V. Tizabi, M. D. Isensee, F. Adler, T. J. Ali, S. Andrearczyk, V. Aubreville, M. Baid, U. Bakas, S. Balu, N. Bano, S. Bernal, J. Bodenstedt, S. Casella, A. Cheplygina, V. Daum, M. de Bruijne, M. Depeursinge, A. Dorent, R. Egger, J. Ellis, D. G. Engelhardt, S. Ganz, M. Ghatwary, N. Girard, G. Godau, P. Gupta, A. Hansen, L. Harada, K. Heinrich, M. Heller, N. Hering, A. Huaulme, A. Jannin, P. Kavur, A. E. Kodym, O. Kozubek, M. Li, J. Li, H. Ma, J. Martin-Isla, C. Menze, B. Noble, A. Oreiller, V. Padoy, N. Pati, S. Payette, K. Raedsch, T. Rafael-Patino, J. Bawa, V. Singh Speidel, S. Sudre, C. H. van Wijnen, K. Wagner, M. Wei, D. Yamlahi, A. Yap, M. H. Yuan, C. Zenk, M. Zia, A. Zimmerer, D. Aydogan, D. Bhattarai, B. Bloch, L. Bruengel, R. Cho, J. Choi, C. Dou, Q. Ezhov, I. Friedrich, C. M. Fuller, C. Gaire, R. R. Galdran, A. Faura, A. Garcia Grammatikopoulou, M. Hong, S. Jahanifar, M. Jang, I. Kadkhodamohammadi, A. Kang, I. Kofler, F. Kondo, S. Kuijf, H. Li, M. Luu, M. Martincic, T. Morais, P. Naser, M. A. Oliveira, B. Owen, D. Pang, S. Park, J. Park, S. Plotka, S. Puybareau, E. Rajpoot, N. Ryu, K. Saeed, N. Shephard, A. Shi, P. Stepec, D. Subedi, R. Tochon, G. Torres, H. R. Urien, H. Vilaca, J. L. Wahid, K. A. Wang, H. Wang, J. Wang, L. Wang, X. Wiestler, B. Wodzinski, M. Xia, F. Xie, J. Xiong, Z. Yang, S. Yang, Y. Zhao, Z. Maier-Hein, K. Jaeger, P. F. Kopp-Schneider, A. Maier-Hein, L. German Canc Res Ctr Div Intelligent Med Syst Heidelberg Germany German Canc Res Ctr Helmholtz Imaging Heidelberg Germany Heidelberg Univ Fac Math & Comp Sci Heidelberg Germany German Canc Res Ctr Div Biostat Heidelberg Germany German Canc Res Ctr Div Med Image Comp Heidelberg Germany Univ Leeds Fac Engn & Phys Sci Sch Comp Leeds W Yorkshire England HES SO Valais Wallis Univ Appl Sci & Arts Western Inst Informat Sch Management Sierre Switzerland Lausanne Univ Hosp Dept Nucl Med & Mol Imaging Lausanne Switzerland Tech Hsch Ingolstadt Ingolstadt Germany Univ Penn Ctr Artificial Intelligence & Data Sci Integrated Philadelphia PA USA Univ Penn Ctr Biomed Image Comp & Analyt Philadelphia PA USA Univ Penn Dept Pathol & Lab Med Perelman Sch Med Philadelphia PA USA Univ Penn Dept Radiol Perelman Sch Med Philadelphia PA USA Univ Washington Dept Radiol Seattle WA USA UCL Wellcome EPSRC Ctr Intervent & Surg Sci WEISS London England UCL Dept Comp Sci London England Univ Autonoma Barcelona Barcelona Spain Comp Vis Ctr Barcelona Spain Natl Ctr Tumor Dis NCT UCC Div Translat Surg Oncol Dresden Germany Ist Italiano Tecnol Dept Adv Robot Genoa Italy Politecn Milan Dept Elect Informat & Bioengn Milan Italy IT Univ Copenhagen Copenhagen Denmark Heidelberg Univ Hosp Dept Gen Visceral & Transplantat Surg Heidelberg Germany Erasmus MC Dept Radiol & Nucl Med Biomed Imaging Grp Rotterdam Rotterdam Netherlands Univ Copenhagen Dept Comp Sci Copenhagen Denmark Univ Appl Sci Western Switzerland HES SO Inst Informat Syst Sierre Switzerland Harvard Med Sch Brigham & Womens Hosp Boston MA USA Kings Coll London Sch Biomed Engn & Imaging Sci London England Univ Hosp Essen AoR Inst Artificial Intelligence Med IKIM Essen Germany Univ Nebraska Med Ctr Omaha NE USA Heidelberg Univ Hosp Dept Internal Med 3 Heidelberg Germany Rigshosp Copenhagen Univ Hosp Neurobiol Res Unit Copenhagen Denmark Arab Acad Sci & Technol Cairo Egypt CIBM Ctr Biomed Imaging L

ISBN: (纸本)9798350301298

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi-center study with all 80 competitions that were conducted in the scope of ieee ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. [GRAPHICS] .

关键词： cell microscopy Medical and biological vision

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

NTIRE 2018 Challenge on Single Image Super-Resolution: Metho...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Radu Timofte Shuhang Gu Jiqing Wu Luc Van Gool Lei Zhang Ming-Hsuan Yang Muhammad Haris Greg Shakhnarovich Norimichi Ukita Shijia Hu Yijie Bei Zheng Hui Xiao Jiang Yanan Gu Jie Liu Yifan Wang Federico Perazzi Brian McWilliams Alexander Sorkine-Hornung Olga Sorkine-Hornung Christopher Schroers Jiahui Yu Yuchen Fan Jianchao Yang Ning Xu Zhaowen Wang Xinchao Wang Thomas S. Huang Xintao Wang Ke Yu Tak-Wai Hui Chao Dong Liang Lin Chen Change Loy Dongwon Park Kwanyoung Kim Se Young Chun Kai Zhang Pengjv Liu Wangmeng Zuo Shi Guo Jiye Liu Jinchang Xu Yijiao Liu Fengye Xiong Yuan Dong Hongliang Bai Alexandru Damian Nikhil Ravi Sachit Menon Cynthia Rudin Junghoon Seo Taegyun Jeon Jamyoung Koo Seunghyun Jeon Soo Ye Kim Jae-Seok Choi Sehwan Ki Soomin Seo Hyeonjun Sim Saehun Kim Munchurl Kim Rong Chen Kun Zeng Jinkang Guo Yanyun Qu Cuihua Li Namhyuk Ahn Byungkon Kang Kyung-Ah Sohn Yuan Yuan Jiawei Zhang Jiahao Pang Xiangyu Xu Yan Zhao Wei Deng Sibt Ul Hussain Muneeb Aadil Rafia Rahim Xiaowang Cai Fang Huang Yueshu Xu Pablo Navarrete Michelini Dan Zhu Hanwen Liu Jun-Hyuk Kim Jong-Seok Lee Yiwen Huang Ming Qiu Liting Jing Jiehang Zeng Ying Wang Manoj Sharma Rudrabha Mukhopadhyay Avinash Upadhyay Sriharsha Koundinya Ankit Shukla Santanu Chaudhury Zhe Zhang Yu Hen Hu Lingzhi Fu ETH Zurich Zurich Switzerland Merantix Germany NTIRE Eidgenossische Technische Hochschule Zurich Zurich ZH CH ESAT KU Leuven Belgium University of California at Merced CA USA Toyota Technological Institute TTI-Chicago Chicago US Duke University US School of Electronic Engineering Xidian University China Disney Research Facebook Oculus University of Illinois at Urbana-Champaign Illinois US Snap Inc. Snap Inc. Adobe Research Stevens Institute of Technology US University of Illinois at Urbana-Champaign US The Chinese University of Hong Kong Hong Kong China SenseTime Research School of Electrical and Computer Engineering National Institute of Science and Technolog Korea School of Electrical and Computer Engineering National Institute of Science and Technology Korea School of Computer Science and Technology Harbin Institute of Technology Harbin China Beijing University of Posts and Telecommunications China Beijing Faceall Technology Co. Ltd China Satrec Initiative R&D Center Korea Advanced Institute of Science and Technology Korea Xiamen University China Xiamen Universi China Ajou University Korea Electrical and Computer Engineering University of Alberta Canada Fuzhou University China National University of Computer & Emerging Sciences Islamabad Pakistan BOE Technology Group Co. Ltd. Yonsei University Korea Wenhua College China Software School Xiamen University China CSIR-CEERI India University of Wisconsin-Madison Madison Wisconsin US Institute of Integrated Automation School of Electronic and Information Engineering Xian Jiaotong University China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661017

This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were learnable through provided pairs of low and high resolution train images. The tracks had 145, 114, 101, and 113 registered participants, resp., and 31 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution.

关键词： Image resolution Degradation Training Runtime computer architecture Testing Tracking

来源：评论

学校读者我要写书评

暂无评论

Why is the Winner the Best?

Why is the Winner the Best?

引用

conference on computer vision and pattern recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of ieee ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

关键词：

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332页 << < 323 324 325 326 327 328 329 330 331 332 > >>

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：