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

29,399 篇 会议
1,396 册 图书
226 篇 期刊文献

馆藏范围

31,019 篇 电子文献
2 种 纸本馆藏

日期分布

学科分类号

17,289 篇 工学
- 13,630 篇 计算机科学与技术...
- 5,205 篇 软件工程
- 2,971 篇 机械工程
- 2,646 篇 光学工程
- 1,413 篇 控制科学与工程
- 1,407 篇 电气工程
- 1,335 篇 信息与通信工程
- 656 篇 生物工程
- 579 篇 仪器科学与技术
- 513 篇 生物医学工程（可授...
- 466 篇 电子科学与技术（可...
- 251 篇 化学工程与技术
- 214 篇 安全科学与工程
- 142 篇 交通运输工程
- 132 篇 建筑学
- 121 篇 材料科学与工程（可...
- 119 篇 土木工程
5,060 篇 理学
- 3,128 篇 物理学
- 2,407 篇 数学
- 824 篇 生物学
- 802 篇 统计学（可授理学、...
- 300 篇 系统科学
- 229 篇 化学
3,832 篇 医学
- 3,800 篇 临床医学
- 187 篇 基础医学(可授医学...
- 140 篇 药学(可授医学、理...
1,061 篇 管理学
- 617 篇 图书情报与档案管...
- 469 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

12,597 篇 computer vision
5,699 篇 pattern recognit...
3,181 篇 training
2,263 篇 cameras
2,178 篇 computational mo...
2,117 篇 feature extracti...
2,049 篇 image segmentati...
1,971 篇 visualization
1,967 篇 shape
1,642 篇 robustness
1,491 篇 layout
1,476 篇 three-dimensiona...
1,442 篇 computer science
1,338 篇 computer archite...
1,294 篇 object detection
1,220 篇 semantics
1,143 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...
95 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

30,855 篇 英文
96 篇 其他
90 篇 中文
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 31021 条记录，以下是4311-4320 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 Position and Target Consistency for Memory-based Video Object Segmentation

Learning Position and Target Consistency for Memory-based Vi...

引用

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

作者： Hu, Li Zhang, Peng Zhang, Bang Pan, Pan Xu, Yinghui Jin, Rong Alibaba Grp Machine Intelligence Technol Lab Hangzhou Peoples R China

ISBN: (纸本)9781665445092

This paper studies the problem of semi-supervised video object segmentation(VOS). Multiple works have shown that memory-based approaches can be effective for video object segmentation. They are mostly based on pixel-level matching, both spatially and temporally. The main shortcoming of memory-based approaches is that they do not take into account the sequential order among frames and do not exploit object-level knowledge from the target. To address this limitation, we propose to Learn position and target Consistency framework for Memory-based video object segmentation, termed as LCM. It applies the memory mechanism to retrieve pixels globally, and meanwhile learns position consistency for more reliable segmentation. The learned location response promotes a better discrimination between target and distractors. Besides, LCM introduces an object-level relationship from the target to maintain target consistency, making LCM more robust to error drifting. Experiments show that our LCM achieves state-of-the-art performance on both DAVIS and Youtube-VOS benchmark. And we rank the 1st in the DAVIS 2020 challenge semi-supervised VOS task.

关键词： computer vision Object segmentation Benchmark testing pattern recognition Reliability Task analysis

来源：评论

学校读者我要写书评

暂无评论

Panoramic Image Reflection Removal

Panoramic Image Reflection Removal

引用

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

作者： Hong, Yuchen Zheng, Qian Zhao, Lingran Jiang, Xudong Kot, Alex C. Shi, Boxin Peking Univ Dept Comp Sci & Technol NELVT Beijing Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Peking Univ Inst Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

This paper studies the problem of panoramic image reflection removal, aiming at reliving the content ambiguity between reflection and transmission scenes. Although a partial view of the reflection scene is included in the panoramic image, it cannot be utilized directly due to its misalignment with the reflection-contaminated image. We propose a two-step approach to solve this problem, by first accomplishing geometric and photometric alignment for the reflection scene via a coarse-to-fine strategy, and then restoring the transmission scene via a recovery network. The proposed method is trained with a synthetic dataset and verified quantitatively with a real panoramic image dataset. The effectiveness of the proposed method is validated by the significant performance advantage over single image-based reflection removal methods and generalization capacity to limited-FoV scenarios captured by conventional camera or mobile phone users.

关键词： computer vision Cameras Reflection Mobile handsets pattern recognition Image restoration

来源：评论

学校读者我要写书评

暂无评论

PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks

PU-GCN: Point Cloud Upsampling using Graph Convolutional Net...

引用

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

作者： Qian, Guocheng Abualshour, Abdulellah Li, Guohao Thabet, Ali Ghanem, Bernard King Abdullah Univ Sci & Technol KAUST Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

The effectiveness of learning-based point cloud upsampling pipelines heavily relies on the upsampling modules and feature extractors used therein. For the point upsampling module, we propose a novel model called NodeShuffle, which uses a Graph Convolutional Network (GCN) to better encode local point information from point neighborhoods. NodeShuffle is versatile and can be incorporated into any point cloud upsampling pipeline. Extensive experiments show how NodeShuffle consistently improves state-of-theart upsampling methods. For feature extraction, we also propose a new multi-scale point feature extractor, called Inception DenseGCN. By aggregating features at multiple scales, this feature extractor enables further performance gain in the final upsampled point clouds. We combine Inception DenseGCN with NodeShuffle into a new point upsampling pipeline called PU-GCN. PU-GCN sets new state-of-art performance with much fewer parameters and more efficient inference. Our code is publicly available at https://***/guochengqian/PU-GCN.

关键词： Convolutional codes computer vision Pipelines Performance gain Feature extraction pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Connecting Language and vision for Natural Language-Based Vehicle Retrieval

Connecting Language and Vision for Natural Language-Based Ve...

引用

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

作者： Bai, Shuai Zheng, Zhedong Wang, Xiaohan Lin, Junyang Zhang, Zhu Zhou, Chang Yang, Hongxia Yang, Yi Alibaba Grp DAMO Acad Hangzhou Peoples R China Univ Technol Sydney ReLER Lab Sydney NSW Australia Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9781665448994

Vehicle search is one basic task for the efficient traffic management in terms of the AI City. Most existing practices focus on the image-based vehicle matching, including vehicle re-identification and vehicle tracking. In this paper, we apply one new modality, i.e., the language description, to search the vehicle of interest and explore the potential of this task in the real-world scenario. The natural language-based vehicle search poses one new challenge of fine-grained understanding of both vision and language modalities. To connect language and vision, we propose to jointly train the state-of-the-art vision models with the transformer-based language model in an end-to-end manner. Except for the network structure design and the training strategy, several optimization objectives are also revisited in this work. The qualitative and quantitative experiments verify the effectiveness of the proposed method. Our proposed method has achieved the 1st place on the 5th AI City Challenge, yielding competitive performance 18.69% MRR accuracy on the private test set. We hope this work can pave the way for the future study on using language description effectively and efficiently for real-world vehicle retrieval systems. The code will be available at https://***/ShuaiBai623/AIC2021-T5-CLV.

关键词： Training Smart cities computer architecture Search problems Robustness pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

Collaborative Image and Object Level Features for Image Colourisation

Collaborative Image and Object Level Features for Image Colo...

引用

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

作者： Pucci, Rita Micheloni, Christian Martinel, Niki Univ Udine Udine Italy

ISBN: (纸本)9781665448994

Image colourisation is an ill-posed problem, with multiple correct solutions which depend on the context and object instances present in the input datum. Previous approaches attacked the problem either by requiring intense user-interactions or by exploiting the ability of convolutional neural networks (CNNs) in learning image-level (context) features. However, obtaining human hints is not always feasible and CNNs alone are not able to learn entity-level semantics, unless multiple models pre-trained with supervision are considered. In this work, we propose a single network, named UCapsNet, that takes into consideration the image-level features obtained through convolutions and entity-level features captured by means of capsules. Then, by skip connections over different layers, we enforce collaboration between such the convolutional and entity factors to produce a high-quality and plausible image colourisation. We pose the problem as a classification task that can be addressed by a fully unsupervised approach, thus requires no human effort. Experimental results on three benchmark datasets show that our approach outperforms existing methods on standard quality metrics and achieves state-of-the-art performances on image colourisation. A large scale user study shows that our method is preferred over existing solutions. Code available at https://***/Riretta/Image_Colourisation_WiCV_2021.

关键词： Convolutional codes computer vision Semantics Collaboration Training data Feature extraction pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Tailored visions: Enhancing Text-to-Image Generation with Personalized Prompt Rewriting

Tailored Visions: Enhancing Text-to-Image Generation with Pe...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zijie Chen Lichao Zhang Fangsheng Weng Lili Pan Zhenzhong Lan Zhejiang University Westlake University Scietrain University of Electronic Science and Technology of China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Despite significant progress in the field, it is still challenging to create personalized visual representations that align closely with the desires and preferences of individ-ual users. This process requires users to articulate their ideas in words that are both comprehensible to the models and accurately capture their vision, posing difficul-ties for many users. In this paper, we tackle this challenge by leveraging historical user interactions with the system to enhance user prompts. We propose a novel approach that involves rewriting user prompts based on a newly collected large-scale text-to-image dataset with over 300k prompts from 3115 users. Our rewriting model enhances the expressiveness and alignment of user prompts with their intended visual outputs. Experimental results demonstrate the superiority of our methods over baseline approaches, as evidenced in our new offline evaluation method and online tests. Our code and dataset are available at https://***/zzjchen/Tailored-visions

关键词： Visualization computer vision Codes Text to image pattern recognition

来源：评论

学校读者我要写书评

暂无评论

No frame left behind: Full Video Action recognition

No frame left behind: Full Video Action Recognition

引用

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

作者： Liu, Xin Pintea, Silvia L. Nejadasl, Fatemeh Karimi Booij, Olaf van Gemert, Jan C. Delft Univ Technol Comp Vis Lab Delft Netherlands TomTom Amsterdam Netherlands

ISBN: (纸本)9781665445092

Not all video frames are equally informative for recognizing an action. It is computationally infeasible to train deep networks on all video frames when actions develop over hundreds of frames. A common heuristic is uniformly sampling a small number of video frames and using these to recognize the action. Instead, here we propose full video action recognition and consider all video frames. To make this computational tractable, we first cluster all frame activations along the temporal dimension based on their similarity with respect to the classification task, and then temporally aggregate the frames in the clusters into a smaller number of representations. Our method is end-to-end trainable and computationally efficient as it relies on temporally localized clustering in combination with fast Hamming distances in feature space. We evaluate on UCF101, HMDB51, Breakfast, and Something-Something V1 and V2, where we compare favorably to existing heuristic frame sampling methods.

关键词： Training computer vision Philosophical considerations Semantics Memory management Sampling methods Nonhomogeneous media

来源：评论

学校读者我要写书评

暂无评论

Unified Face Attack Detection with Micro Disturbance and a Two-Stage Training Strategy

Unified Face Attack Detection with Micro Disturbance and a T...

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Jiaruo Yu Dagong Lu Xingyue Shi Chenfan Qu Fengjun Guo IntSig Information Co. Ltd Shanghai China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face recognition systems are widely used in real-world scenarios but are susceptible to physical and digital attacks. Effective methods for unified detection of both physical face attacks and digital face attacks are essential to ensure the reliability of face recognition systems. However, how to obtain a unified face attack detection model that has adequate ability of fine-grained perception and cross-domain generalization ability remains an open challenge. To address this issue, we first propose a two-stage training strategy, which utilizes unlabeled face images with masked image modeling and unleashes the potential of vision transformers. Furthermore, we propose a novel method termed as Micro Disturbance, which successfully enriches the representation distribution of forged faces and increases the diversity of the training data, thereby addressing the issue of cross-domain generalization. Attribute to the effectiveness of our proposed methods, our model finally wins the third place in the 5th Face Anti-Spoofing Challenge@CVPR2024, with an impressive ACER score of 5.511.

关键词： Training computer vision Social networking (online) Face recognition Forensics conferences Training data

来源：评论

学校读者我要写书评

暂无评论

CompositeTasking: Understanding Images by Spatial Composition of Tasks

CompositeTasking: Understanding Images by Spatial Compositio...

引用

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

作者： Popovic, Nikola Paudel, Danda Pani Probst, Thomas Sun, Guolei Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Katholieke Univ Leuven ESAT PSI VISICS Leuven Belgium

ISBN: (纸本)9781665445092

We define the concept of CompositeTasking as the fusion of multiple, spatially distributed tasks, for various aspects of image understanding. Learning to perform spatially distributed tasks is motivated by the frequent availability of only sparse labels across tasks, and the desire for a compact multi-tasking network. To facilitate CompositeTasking, we introduce a novel task conditioning model - a single encoder-decoder network that performs multiple, spatially varying tasks at once. The proposed network takes an image and a set of pixel-wise dense task requests as inputs, and performs the requested prediction task for each pixel. Moreover, we also learn the composition of tasks that needs to be performed according to some CompositeTasking rules, which includes the decision of where to apply which task. It not only offers us a compact network for multi-tasking, but also allows for task-editing. Another strength of the proposed method is demonstrated by only having to supply sparse supervision per task. The obtained results are on par with our baselines that use dense supervision and a multi-headed multi-tasking design. The source code will be made publicly available at ***/nikola3794/composite-tasking.

关键词： computer vision Codes Multitasking pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

Achieving robustness in classification using optimal transport with hinge regularization

Achieving robustness in classification using optimal transpo...

引用

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

作者： Serrurier, Mathieu Mamalet, Franck Gonzalez-Sanz, Alberto Boissin, Thibaut Loubes, Jean-Michel del Barrio, Eustasio Univ Paul Sabatier Toulouse France IRT St Exupery Toulouse France Univ Valladolid Valladolid Spain

ISBN: (纸本)9781665445092

Adversarial examples have pointed out Deep Neural Network's vulnerability to small local noise. It has been shown that constraining their Lipschitz constant should enhance robustness, but make them harder to learn with classical loss functions. We propose a new framework for binary classification, based on optimal transport, which integrates this Lipschitz constraint as a theoretical requirement. We propose to learn 1-Lipschitz networks using a new loss that is an hinge regularized version of the Kantorovich-Rubinstein dual formulation for the Wasserstein distance estimation. This loss function has a direct interpretation in terms of adversarial robustness together with certifiable robustness bound. We also prove that this hinge regularized version is still the dual formulation of an optimal transportation problem, and has a solution. We also establish several geometrical properties of this optimal solution, and extend the approach to multi-class problems. Experiments show that the proposed approach provides the expected guarantees in terms of robustness without any significant accuracy drop. The adversarial examples, on the proposed models, visibly and meaningfully change the input providing an explanation for the classification.

关键词： computer vision Computational modeling Transportation Estimation Fasteners Robustness pattern recognition

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 << < 428 429 430 431 432 433 434 435 436 437 > >>

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：