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

29,393 篇 会议
1,358 册 图书
225 篇 期刊文献

馆藏范围

30,974 篇 电子文献
2 种 纸本馆藏

日期分布

学科分类号

17,280 篇 工学
- 13,630 篇 计算机科学与技术...
- 5,193 篇 软件工程
- 2,983 篇 机械工程
- 2,643 篇 光学工程
- 1,421 篇 控制科学与工程
- 1,411 篇 电气工程
- 1,344 篇 信息与通信工程
- 656 篇 生物工程
- 577 篇 仪器科学与技术
- 513 篇 生物医学工程（可授...
- 468 篇 电子科学与技术（可...
- 251 篇 化学工程与技术
- 212 篇 安全科学与工程
- 140 篇 交通运输工程
- 132 篇 建筑学
- 123 篇 材料科学与工程（可...
- 119 篇 土木工程
5,054 篇 理学
- 3,127 篇 物理学
- 2,406 篇 数学
- 824 篇 生物学
- 802 篇 统计学（可授理学、...
- 299 篇 系统科学
- 228 篇 化学
3,831 篇 医学
- 3,799 篇 临床医学
- 185 篇 基础医学(可授医学...
- 140 篇 药学(可授医学、理...
1,059 篇 管理学
- 617 篇 图书情报与档案管...
- 467 篇 管理科学与工程(可...
- 145 篇 工商管理
373 篇 艺术学
- 373 篇 设计学（可授艺术学...
116 篇 法学
81 篇 农学
48 篇 教育学
43 篇 经济学
18 篇 军事学
8 篇 文学

主题

12,602 篇 computer vision
5,697 篇 pattern recognit...
3,180 篇 training
2,263 篇 cameras
2,178 篇 computational mo...
2,116 篇 feature extracti...
2,048 篇 image segmentati...
1,970 篇 visualization
1,967 篇 shape
1,642 篇 robustness
1,493 篇 layout
1,476 篇 three-dimensiona...
1,445 篇 computer science
1,338 篇 computer archite...
1,296 篇 object detection
1,220 篇 semantics
1,142 篇 face recognition
1,107 篇 conferences
1,077 篇 benchmark testin...
1,056 篇 humans

机构

137 篇 univ sci & techn...
134 篇 tsinghua univers...
134 篇 univ chinese aca...
118 篇 chinese univ hon...
101 篇 microsoft resear...
97 篇 zhejiang univers...
94 篇 national laborat...
93 篇 shanghai jiao to...
93 篇 zhejiang univ pe...
85 篇 university of sc...
79 篇 shanghai ai lab ...
78 篇 swiss fed inst t...
65 篇 microsoft res as...
62 篇 adobe research
62 篇 computer vision ...
61 篇 peking univ peop...
58 篇 univ oxford oxfo...
57 篇 google mountain ...
57 篇 hong kong univ s...
56 篇 google res mount...

作者

107 篇 umapada pal
81 篇 van gool luc
68 篇 zhang lei
59 篇 timofte radu
41 篇 yang yi
37 篇 loy chen change
37 篇 hanqing lu
33 篇 liu yang
33 篇 xiaoou tang
32 篇 nassir navab
32 篇 wang liang
30 篇 tian qi
29 篇 h. bischof
29 篇 jan-michael frah...
29 篇 vittorio murino
29 篇 darrell trevor
27 篇 li xin
27 篇 vasconcelos nuno
27 篇 murino vittorio
27 篇 chen chen

语言

30,833 篇 英文
92 篇 中文
73 篇 其他
6 篇 土耳其文
2 篇 日文
2 篇 俄文
1 篇 西班牙文

检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

共 30976 条记录，以下是4751-4760 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：

Learning Generative Structure Prior for Blind Text Image Super-resolution

Learning Generative Structure Prior for Blind Text Image Sup...

引用

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

作者： Li, Xiaoming Zuo, Wangmeng Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore Harbin Inst Technol Harbin Peoples R China

ISBN: (纸本)9798350301298

Blind text image super-resolution (SR) is challenging as one needs to cope with diverse font styles and unknown degradation. To address the problem, existing methods perform character recognition in parallel to regularize the SR task, either through a loss constraint or intermediate feature condition. Nonetheless, the high-level prior could still fail when encountering severe degradation. The problem is further compounded given characters of complex structures, e.g., Chinese characters that combine multiple pictographic or ideographic symbols into a single character. In this work, we present a novel prior that focuses more on the character structure. In particular, we learn to encapsulate rich and diverse structures in a StyleGAN and exploit such generative structure priors for restoration. To restrict the generative space of StyleGAN so that it obeys the structure of characters yet remains flexible in handling different font styles, we store the discrete features for each character in a codebook. The code subsequently drives the StyleGAN to generate high-resolution structural details to aid text SR. Compared to priors based on character recognition, the proposed structure prior exerts stronger character-specific guidance to restore faithful and precise strokes of a designated character. Extensive experiments on synthetic and real datasets demonstrate the compelling performance of the proposed generative structure prior in facilitating robust text SR. Our code is available at https://***/csxmli2016/MARCONet.

关键词： Low-level vision

来源：评论

学校读者我要写书评

暂无评论

Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for vision-Language Navigation 32

Reinforced Cross-Modal Matching and Self-Supervised Imitatio...

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Xin Huang, Qiuyuan Celikyilmaz, Asli Gao, Jianfeng Shen, Dinghan Wang, Yuan-Fang Wang, William Yang Zhang, Lei Univ Calif Santa Barbara Santa Barbara CA 93106 USA Microsoft Res Redmond WA USA Duke Univ Durham NC 27706 USA

ISBN: (纸本)9781728132938

vision-language navigation (VLN) is the task of navigating an embodied agent to carry out natural language instructions inside real 3D environments. In this paper, we study how to address three critical challenges for this task: the cross-modal grounding, the ill-posed feedback, and the generalization problems. First, we propose a novel Reinforced Cross-Modal Matching (RCM) approach that enforces cross-modal grounding both locally and globally via reinforcement learning (RL). Particularly, a matching critic is used to provide an intrinsic reward to encourage global matching between instructions and tV rajectories, and a reasoning navigator is employed to perform cross-modal grounding in the local visual scene. Evaluation on a VLN benchmark dataset shows that our RCM model significantly outperforms previous methods by 10% on SPL and achieves the new state-of-the-art performance. To improve the generalizability of the learned policy, we further introduce a Self-Supervised Imitation Learning (SIL) method to explore unseen environments by imitating its own past, good decisions. We demonstrate that SIL can approximate a better and more efficient policy, which tremendously minimizes the success rate performance gap between seen and unseen environments (from 30.7% to 11.7%).

关键词： Robotics + Driving vision + Language vision Applications and Systems Visual Reasoning

来源：评论

学校读者我要写书评

暂无评论

Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyramid Deformable Convolution and Deblurring-Reblurring Consistency

Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyr...

引用

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

作者： Huo, Dong Masoumzadeh, Abbas Yang, Yee-Hong Univ Alberta Dept Comp Sci Edmonton AB Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Many deep learning based methods are designed to remove non-uniform (spatially variant) motion blur caused by object motion and camera shake without knowing the blur kernel. Some methods directly output the latent sharp image in one stage, while others utilize a multi-stage strategy (e.g. multi-scale, multi-patch, or multi-temporal) to gradually restore the sharp image. However, these methods have the following two main issues: 1) The computational cost of multi-stage is high;2) The same convolution kernel is applied in different regions, which is not an ideal choice for non-uniform blur. Hence, non-uniform motion deblurring is still a challenging and open problem. In this paper, we propose a new architecture which consists of multiple Atrous Spatial Pyramid Deformable Convolution (ASPDC) modules to deblur an image end-to-end with more flexibility. Multiple ASPDC modules implicitly learn the pixel-specific motion with different dilation rates in the same layer to handle movements of different magnitude. To improve the training, we also propose a reblurring network to map the deblurred output back to the blurred input, which constrains the solution space. Our experimental results show that the proposed method outperforms state-of-the-art methods on the benchmark datasets. The code is available at https://***/Dong-Huo/ASPDC.

关键词： Training Degradation Deep learning computer vision Convolution Image color analysis conferences

来源：评论

学校读者我要写书评

暂无评论

Not All Relations are Equal: Mining Informative Labels for Scene Graph Generation

Not All Relations are Equal: Mining Informative Labels for S...

引用

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

作者： Goel, Arushi Fernando, Basura Keller, Frank Bilen, Hakan Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland ASTAR IHPC CFAR Singapore Singapore

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Scene graph generation (SGG) aims to capture a wide variety of interactions between pairs of objects, which is essential for full scene understanding. Existing SGG methods trained on the entire set of relations fail to acquire complex reasoning about visual and textual correlations due to various biases in training data. Learning on trivial relations that indicate generic spatial configuration like 'on' instead of informative relations such as 'parked on' does not enforce this complex reasoning, harming generalization. To address this problem, we propose a novel framework for SGG training that exploits relation labels based on their informativeness. Our model-agnostic training procedure imputes missing informative relations for less informative samples in the training data and trains a SGG model on the imputed labels along with existing annotations. We show that this approach can successfully be used in conjunction with state-of-the-art SGG methods and improves their performance significantly in multiple metrics on the standard Visual Genome benchmark. Furthermore, we obtain considerable improvements for unseen triplets in a more challenging zero-shot setting.

关键词： Training Measurement Visualization Training data Data models Cognition pattern recognition

来源：评论

学校读者我要写书评

暂无评论

FLOAT: Factorized Learning of Object Attributes for Improved Multi-object Multi-part Scene Parsing

FLOAT: Factorized Learning of Object Attributes for Improved...

引用

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

作者： Singh, Rishubh Gupta, Pranav Shenoy, Pradeep Sarvadevabhatla, Ravikiran Google Res Mountain View CA 94043 USA IIIT Hyderabad Hyderabad Telangana India

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Multi-object multi-part scene parsing is a challenging task which requires detecting multiple object classes in a scene and segmenting the semantic parts within each object. In this paper, we propose FLOAT, a factorized label space framework for scalable multi-object multi-part parsing. Our framework involves independent dense prediction of object category and part attributes which increases scalability and reduces task complexity compared to the monolithic label space counterpart. In addition, we propose an inference-time 'zoom' refinement technique which significantly improves segmentation quality, especially for smaller objects/parts. Compared to state of the art, FLOAT obtains an absolute improvement of 2.0% for mean IOU (mIOU) and 4.8% for segmentation quality IOU (sqIOU) on the Pascal-Part-58 dataset. For the larger Pascal-Part-108 dataset, the improvements are 2.1% for mIOU and 3.9% for sqIOU. We incorporate previously excluded part attributes and other minor parts of the Pascal-Part dataset to create the most comprehensive and challenging version which we dub Pascal-Part-201. FLOAT obtains improvements of 8.6% for mIOU and 7.5% for sqIOU on the new dataset, demonstrating its parsing effectiveness across a challenging diversity of objects and parts. The code and datasets are available at ***.

关键词： computer vision Three-dimensional displays Codes Scalability Semantics Focusing Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Joint Video Summarization and Moment Localization by Cross-Task Sample Transfer

Joint Video Summarization and Moment Localization by Cross-T...

引用

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

作者： Jiang, Hao Mu, Yadong Peking Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Video summarization has recently engaged increasing attention in computer vision communities. However, the scarcity of annotated data has been a key obstacle in this task. To address it, this work explores a new solution for video summarization by transferring samples from a correlated task (i.e., video moment localization) equipped with abundant training data. Our main insight is that the annotated video moments also indicate the semantic highlights of a video, essentially similar to video summary. Approximately, the video summary can be treated as a sparse, redundancy free version of the video moments. Inspired by this observation, we propose an importance Propagation based collaborative Teaching Network (iPTNet). It consists of two separate modules that conduct video summarization and moment localization, respectively. Each module estimates a frame-wise importance map for indicating keyframes or moments. To perform cross-task sample transfer, we devise an importance propagation module that realizes the conversion between summarization-guided and localization-guided importance maps. This way critically enables optimizing one of the tasks using the data from the other task. Additionally, in order to avoid error amplification caused by batch-wise joint training, we devise a collaborative teaching scheme, which adopts a cross-task mean teaching strategy to realize the joint optimization of the two tasks and provide robust frame-level teaching signals. Extensive experiments on video summarization benchmarks demonstrate that iPTNet significantly outperforms previous state-of-the-art video summarization methods, serving as an effective solution that overcomes the data scarcity issue in video summarization.

关键词： Location awareness Training computer vision Machine vision Semantics Collaboration Training data

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

HandOccNet: Occlusion-Robust 3D Hand Mesh Estimation Network

HandOccNet: Occlusion-Robust 3D Hand Mesh Estimation Network

引用

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

作者： Park, JoonKyu Oh, Yeonguk Moon, Gyeongsik Choi, Hongsuk Lee, Kyoung Mu Seoul Natl Univ Dept ECE & ASRI Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Hands are often severely occluded by objects, which makes 3D hand mesh estimation challenging. Previous works often have disregarded information at occluded regions. However, we argue that occluded regions have strong correlations with hands so that they can provide highly beneficial information for complete 3D hand mesh estimation. Thus, in this work, we propose a novel 3D hand mesh estimation network HandOccNet, that can fully exploits the information at occluded regions as a secondary means to enhance image features and make it much richer. To this end, we design two successive Transformer-based modules, called feature injecting transformer (FIT) and self-enhancing transformer (SET). FIT injects hand information into occluded region by considering their correlation. SET refines the output of FIT by using a self-attention mechanism. By injecting the hand information to the occluded region, our HandOccNet reaches the state-of-the-art performance on 3D hand mesh benchmarks that contain challenging hand-object occlusions. The codes are available in: https://***/namepllet/HandOccNet.

关键词： computer vision Three-dimensional displays Correlation Codes Face recognition Estimation Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Blueprint Separable Residual Network for Efficient Image Super-Resolution

Blueprint Separable Residual Network for Efficient Image Sup...

引用

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

作者： Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Gu, Jinjin Qiao, Yu Dong, Chao Chinese Acad Sci ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol SIAT SenseTime Joint Lab Beijing Peoples R China Univ Macau Taipa Macao Peoples R China Shanghai AI Lab Shanghai Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent advances in single image super-resolution (SISR) have achieved extraordinary performance, but the computational cost is too heavy to apply in edge devices. To alleviate this problem, many novel and effective solutions have been proposed. Convolutional neural network (CNN) with the attention mechanism has attracted increasing attention due to its efficiency and effectiveness. However, there is still redundancy in the convolution operation. In this paper, we propose Blueprint Separable Residual Network (BSRN) containing two efficient designs. One is the usage of blueprint separable convolution (BSConv), which takes place of the redundant convolution operation. The other is to enhance the model ability by introducing more effective attention modules. The experimental results show that BSRN achieves state-of-the-art performance among existing efficient SR methods. Moreover, a smaller variant of our model BSRN-S won the first place in model complexity track of NTIRE 2022 Efficient SR Challenge. The code is available at https://***/xiaom233/BSRN.

关键词： Performance evaluation Convolution Computational modeling Superresolution Redundancy Complexity theory pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Pareidolia Face Reenactment

Pareidolia Face Reenactment

引用

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

作者： Song, Linsen Wu, Wayne Fu, Chaoyou Qian, Chen Loy, Chen Change He, Ran Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China CASIA NLPR & CRIPAC Beijing Peoples R China SenseTime Res Hong Kong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9781665445092

We present a new application direction named Pareidolia Face Reenactment, which is defined as animating a static illusory face to move in tandem with a human face in the video. For the large differences between pareidolia face reenactment and traditional human face reenactment, two main challenges are introduced, i.e., shape variance and texture variance. In this work, we propose a novel Parametric Unsupervised Reenactment Algorithm to tackle these two challenges. Specifically, we propose to decompose the reenactment into three catenate processes: shape modeling, motion transfer and texture synthesis. With the decomposition, we introduce three crucial components, i.e., Parametric Shape Modeling, Expansionary Motion Transfer and Unsupervised Texture Synthesizer, to overcome the problems brought by the remarkably variances on pareidolia faces. Extensive experiments show the superior performance of our method both qualitatively and quantitatively. Code, model and data are available on our project page(1).

关键词： computer vision Codes Shape Face recognition Computational modeling Synthesizers Data models

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 << < 472 473 474 475 476 477 478 479 480 481 > >>

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：