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

12,844 篇 会议
13 篇 期刊文献
2 册 图书

馆藏范围

12,859 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

7,573 篇 工学
- 6,863 篇 计算机科学与技术...
- 880 篇 机械工程
- 814 篇 软件工程
- 435 篇 控制科学与工程
- 360 篇 光学工程
- 306 篇 电气工程
- 209 篇 仪器科学与技术
- 124 篇 信息与通信工程
- 91 篇 生物工程
- 62 篇 生物医学工程（可授...
- 39 篇 电子科学与技术（可...
- 34 篇 安全科学与工程
- 26 篇 化学工程与技术
- 21 篇 交通运输工程
- 20 篇 建筑学
- 18 篇 土木工程
2,957 篇 医学
- 2,956 篇 临床医学
- 15 篇 基础医学(可授医学...
- 12 篇 药学(可授医学、理...
700 篇 理学
- 359 篇 物理学
- 225 篇 数学
- 175 篇 系统科学
- 95 篇 统计学（可授理学、...
- 93 篇 生物学
- 22 篇 化学
201 篇 艺术学
- 201 篇 设计学（可授艺术学...
84 篇 管理学
- 59 篇 图书情报与档案管...
- 25 篇 管理科学与工程(可...
- 14 篇 工商管理
23 篇 法学
- 21 篇 社会学
5 篇 农学
4 篇 教育学
2 篇 经济学
1 篇 军事学

主题

6,464 篇 computer vision
2,688 篇 training
2,437 篇 pattern recognit...
1,780 篇 computational mo...
1,522 篇 visualization
1,348 篇 three-dimensiona...
1,091 篇 computer archite...
1,063 篇 semantics
997 篇 benchmark testin...
976 篇 codes
970 篇 conferences
854 篇 feature extracti...
830 篇 cameras
771 篇 task analysis
707 篇 deep learning
646 篇 image segmentati...
611 篇 object detection
595 篇 shape
554 篇 transformers
538 篇 neural networks

机构

132 篇 univ sci & techn...
122 篇 carnegie mellon ...
120 篇 tsinghua univ pe...
114 篇 univ chinese aca...
113 篇 chinese univ hon...
94 篇 tsinghua univers...
91 篇 zhejiang univ pe...
91 篇 swiss fed inst t...
85 篇 peng cheng lab p...
81 篇 university of ch...
80 篇 zhejiang univers...
77 篇 shanghai ai lab ...
77 篇 peng cheng labor...
75 篇 university of sc...
69 篇 shanghai jiao to...
68 篇 shanghai jiao to...
67 篇 alibaba grp peop...
67 篇 stanford univ st...
66 篇 univ hong kong p...
64 篇 sensetime res pe...

作者

77 篇 timofte radu
63 篇 van gool luc
45 篇 zhang lei
36 篇 yang yi
36 篇 luc van gool
34 篇 tao dacheng
31 篇 loy chen change
29 篇 chen chen
28 篇 sun jian
28 篇 qi tian
25 篇 li xin
24 篇 liu yang
24 篇 tian qi
24 篇 ying shan
23 篇 wang xinchao
23 篇 zha zheng-jun
23 篇 boxin shi
21 篇 zhou jie
21 篇 vasconcelos nuno
20 篇 luo ping

语言

12,851 篇 英文
7 篇 其他
1 篇 中文

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

共 12859 条记录，以下是4931-4940 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：

STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering

STaR: Self-supervised Tracking and Reconstruction of Rigid O...

引用

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

作者： Yuan, Wentao Lv, Zhaoyang Schmidt, Tanner Lovegrove, Steven Univ Washington Seattle WA 98195 USA Facebook Real Labs Res Menlo Pk CA USA

ISBN: (纸本)9781665445092

We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotation. Recent work has shown that neural networks are surprisingly effective at the task of compressing many views of a scene into a learned function which maps from a viewing ray to an observed radiance value via volume rendering. Unfortunately, these methods lose all their predictive power once any object in the scene has moved. In this work, we explicitly model rigid motion of objects in the context of neural representations of radiance fields. We show that without any additional human specified supervision, we can reconstruct a dynamic scene with a single rigid object in motion by simultaneously decomposing it into its two constituent parts and encoding each with its own neural representation. We achieve this by jointly optimizing the parameters of two neural radiance fields and a set of rigid poses which align the two fields at each frame. On both synthetic and real world datasets, we demonstrate that our method can render photorealistic novel views, where novelty is measured on both spatial and temporal axes. Our factored representation furthermore enables animation of unseen object motion.

关键词： Tracking Dynamics Neural networks Lighting Transforms Manuals Rendering (computer graphics)

来源：评论

学校读者我要写书评

暂无评论

Nearest Neighbor Matching for Deep Clustering

Nearest Neighbor Matching for Deep Clustering

引用

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

作者： Dang, Zhiyuan Deng, Cheng Yang, Xu Wei, Kun Huang, Heng Xidian Univ Sch Elect Engn Xian 710071 Peoples R China JD Tech Beijing 100176 Peoples R China Univ Pittsburgh Dept Elect & Comp Engn Pittsburgh PA 15260 USA JD Finance Amer Corp Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Deep clustering gradually becomes an important branch in unsupervised learning methods. However, current approaches hardly take into consideration the semantic sample relationships that existed in both local and global features. In addition, since the deep features are updated on-the-fly, relying on these sample relationships may construct more semantically confident sample pairs, leading to inferior performance. To tackle this issue, we propose a method called Nearest Neighbor Matching (NNM) to match samples with their nearest neighbors from both local (batch) and global (overall) levels. Specifically, for the local level, we match the nearest neighbors based on batch embedded features, as for the global one, we match neighbors from overall embedded features. To keep the clustering assignment consistent in both neighbors and classes, we frame consistent loss and class contrastive loss for both local and global levels. Experimental results on three benchmark datasets demonstrate the superiority of our new model against state-of-the-art methods. Particularly on the STL-10 dataset, our method can achieve supervised performance. As for the CIFAR-100 dataset, our NNM leads 3.7% against the latest comparison method. Our code will be available at https://***/ZhiyuanDang/NNM.

关键词： computer vision Codes Semantics Artificial neural networks Benchmark testing pattern recognition Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Domain Adaptation with Auxiliary Target Domain-Oriented Classifier

Domain Adaptation with Auxiliary Target Domain-Oriented Clas...

引用

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

作者： Liang, Jian Hu, Dapeng Feng, Jiashi Natl Univ Singapore NUS Singapore Singapore Sea AI Lab SAIL Singapore Singapore

ISBN: (纸本)9781665445092

Domain adaptation (DA) aims to transfer knowledge from a label-rich but heterogeneous domain to a label-scare domain, which alleviates the labeling efforts and attracts considerable attention. Different from previous methods focusing on learning domain-invariant feature representations, some recent methods present generic semi-supervised learning (SSL) techniques and directly apply them to DA tasks, even achieving competitive performance. One of the most popular SSL techniques is pseudo-labeling that assigns pseudo labels for each unlabeled data via the classifier trained by labeled data. However, it ignores the distribution shift in DA problems and is inevitably biased to source data. To address this issue, we propose a new pseudo-labeling framework called Auxiliary Target Domain-Oriented Classifier (ATDOC). ATDOC alleviates the classifier bias by introducing an auxiliary classifier for target data only, to improve the quality of pseudo labels. Specifically, we employ the memory mechanism and develop two types of non-parametric classifiers, i.e. the nearest centroid classifier and neighborhood aggregation, without introducing any additional network parameters. Despite its simplicity in a pseudo classification objective, ATDOC with neighborhood aggregation significantly outperforms domain alignment techniques and prior SSL techniques on a large variety of DA benchmarks and even scare-labeled SSL tasks.

关键词： computer vision Handheld computers Semantics Focusing Semisupervised learning Benchmark testing pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Animating General Image with Large Visual Motion Model

Animating General Image with Large Visual Motion Model

引用

conference on computer vision and pattern recognition (CVPR)

作者： Dengsheng Chen Xiaoming Wei Xiaolin Wei Meituan Beijing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present the pioneering Large Visual Motion Model (LVMM), meticulously engineered to analyze the intrinsic dynamics encapsulated within real-world imagery. Our model, fortified with a wealth of prior knowledge extracted from billions of image pairs, demonstrates promising results in predicting a diverse spectrum of scene dynamics. As a result, it can infuse any generic image with authentic dynamic effects, enhancing its visual allure. Project page: https://***/densechen/LVMM.

关键词： Visualization computer vision Analytical models Computational modeling Dynamics Predictive models pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Understanding the Robustness of Skeleton-based Action recognition under Adversarial Attack

Understanding the Robustness of Skeleton-based Action Recogn...

引用

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

作者： Wang, He He, Feixiang Peng, Zhexi Shao, Tianjia Yang, Yong-Liang Zhou, Kun Hogg, David Univ Leeds Leeds W Yorkshire England Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Univ Bath Bath Avon England

ISBN: (纸本)9781665445092

Action recognition has been heavily employed in many applications such as autonomous vehicles, surveillance, etc, where its robustness is a primary concern. In this paper, we examine the robustness of state-of-the-art action recognizers against adversarial attack, which has been rarely investigated so far. To this end, we propose a new method to attack action recognizers which rely on the 3D skeletal motion. Our method involves an innovative perceptual loss which ensures the imperceptibility of the attack. Empirical studies demonstrate that our method is effective in both white-box and black-box scenarios. Its generalizability is evidenced on a variety of action recognizers and datasets. Its versatility is shown in different attacking strategies. Its deceitfulness is proven in extensive perceptual studies. Our method shows that adversarial attack on 3D skeletal motions, one type of time-series data, is significantly different from traditional adversarial attack problems. Its success raises serious concern on the robustness of action recognizers and provides insights on potential improvements.

关键词： computer vision Three-dimensional displays Surveillance Robustness pattern recognition Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Frequency-aware Discriminative Feature Learning Supervised by Single-Center Loss for Face Forgery Detection

Frequency-aware Discriminative Feature Learning Supervised b...

引用

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

作者： Li, Jiaming Xie, Hongtao Li, Jiahong Wang, Zhongyuan Zhang, Yongdong Univ Sci & Technol China Hefei Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

Face forgery detection is raising ever-increasing interest in computer vision since facial manipulation technologies cause serious worries. Though recent works have reached sound achievements, there are still unignorable problems: a) learned features supervised by softmax loss are separable but not discriminative enough, since softmax loss does not explicitly encourage intra-class compactness and interclass separability;and b)fixed filter banks and hand-crafted features are insufficient to capture forgery patterns of frequency from diverse inputs. To compensate for such limitations, a novel frequency-aware discriminative feature learning framework is proposed in this paper. Specifically, we design a novel single-center loss (SCL) that only compresses intra-class variations of natural faces while boosting interclass differences in the embedding space. In such a case, the network can learn more discriminative features with less optimization difficulty. Besides, an adaptive frequency feature generation module is developed to mine frequency clues in a completely data-driven fashion. With the above two modules, the whole framework can learn more discriminative features in an end-to-end manner. Extensive experiments demonstrate the effectiveness and superiority of our framework on three versions of the FF++ dataset.

关键词： Measurement computer vision Face recognition Frequency-domain analysis Filter banks Boosting Forgery

来源：评论

学校读者我要写书评

暂无评论

Learning a Non-blind Deblurring Network for Night Blurry Images

Learning a Non-blind Deblurring Network for Night Blurry Ima...

引用

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

作者： Chen, Liang Zhang, Jiawei Pan, Jinshan Lin, Songnan Fang, Faming Ren, Jimmy S. East China Normal Univ Sch Comp Sci & Technol Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China SenseTime Res Hong Kong Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China Shanghai Jiao Tong Univ Qing Yuan Res Inst Shanghai Peoples R China SenseTime Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Deblurring night blurry images is difficult, because the common-used blur model based on the linear convolution operation does not hold in this situation due to the influence of saturated pixels. In this paper, we propose a non-blind deblurring network (NBDN) to restore night blurry images. To mitigate the side effects brought by the pixels that violate the blur model, we develop a confidence estimation unit (CEU) to estimate a map which ensures smaller contributions of these pixels in the deconvolution steps which are optimized by the conjugate gradient (CG) method. Moreover, unlike the existing methods using manually tuned hyper-parameters in their frameworks, we propose a hyper-parameter estimation unit (HPEU) to adaptively estimate hyper-parameters for better image restoration. The experimental results demonstrate that the proposed network performs favorably against state-of-the-art algorithms both quantitatively and qualitatively.

关键词： computer vision Deconvolution Convolution Noise reduction Estimation Image restoration pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective Interpolation Training for Robustness to Label Noise

Multi-Objective Interpolation Training for Robustness to Lab...

引用

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

作者： Ortego, Diego Arazo, Eric Albert, Paul O'Connor, Noel E. McGuinness, Kevin Dublin City Univ Insight Ctr Data Analyt Dublin Ireland

ISBN: (纸本)9781665445092

Deep neural networks trained with standard cross-entropy loss memorize noisy labels, which degrades their performance. Most research to mitigate this memorization proposes new robust classification loss functions. Conversely, we propose a Multi-Objective Interpolation Training (MOIT) approach that jointly exploits contrastive learning and classification to mutually help each other and boost performance against label noise. We show that standard supervised contrastive learning degrades in the presence of label noise and propose an interpolation training strategy to mitigate this behavior. We further propose a novel label noise detection method that exploits the robust feature representations learned via contrastive learning to estimate per-sample soft-labels whose disagreements with the original labels accurately identify noisy samples. This detection allows treating noisy samples as unlabeled and training a classifier in a semi-supervised manner to prevent noise memorization and improve representation learning. We further propose MOIT+, a refinement of MOLT by fine-tuning on detected clean samples. Hyperparameter and ablation studies verify the key components of our method. Experiments on synthetic and real-world noise benchmarks demonstrate that MOIT/MOIT+ achieves state-of-the-art results.

关键词： Training Deep learning Interpolation Prototypes Robustness Noise robustness pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Background Splitting: Finding Rare Classes in a Sea of Background

Background Splitting: Finding Rare Classes in a Sea of Backg...

引用

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

作者： Mullapudi, Ravi Teja Poms, Fait Mark, William R. Ramanan, Deva Fatahalian, Kayvon Stanford Univ Stanford CA 94305 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

We focus on the problem of training deep image classification models for a small number of extremely rare categories. In this common, real-world scenario, almost all images belong to the background category in the dataset. We find that state-of-the-art approaches for training on imbalanced datasets do not produce accurate deep models in this regime. Our solution is to split the large, visually diverse background into many smaller, visually similar categories during training. We implement this idea by extending an image classification model with an additional auxiliary loss that learns to mimic the predictions of a pre-existing classification model on the training set. The auxiliary loss requires no additional human labels and regularizes feature learning in the shared network trunk by forcing the model to discriminate between auxiliary categories for all training set examples, including those belonging to the monolithic background of the main rare category classification task. To evaluate our method we contribute modified versions of the iNaturalist and Places365 datasets where only a small subset of rare category labels are available during training (all other images are labeled as background). By jointly learning to recognize both the selected rare categories and auxiliary categories, our approach yields models that perform 8.3 mAP points higher than stateof-the-art imbalanced learning baselines when 98.30% of the data is background, and up to 42.3 mAP points higher than fine-tuning baselines when 99.98% of the data is background.

关键词： Training computer vision Computational modeling Predictive models Data models pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results

NTIRE 2023 Challenge on Light Field Image Super-Resolution: ...

引用

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

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany Eth Zürich Switzerland Sun Yat-sen University The Shenzhen Campus of Sun Yat-sen University China Bigo Technology Pte. Ltd. Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

ISBN: (纸本)9798350302493

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023 ieee.

关键词： Image enhancement

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 << < 490 491 492 493 494 495 496 497 498 499 > >>

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：