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

19,438 篇 会议
46 篇 期刊文献
5 册 图书

馆藏范围

19,488 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

12,440 篇 工学
- 10,282 篇 计算机科学与技术...
- 2,395 篇 机械工程
- 2,007 篇 软件工程
- 813 篇 光学工程
- 531 篇 电气工程
- 419 篇 控制科学与工程
- 322 篇 信息与通信工程
- 210 篇 测绘科学与技术
- 80 篇 生物医学工程（可授...
- 73 篇 电子科学与技术（可...
- 70 篇 生物工程
- 60 篇 仪器科学与技术
- 38 篇 建筑学
- 36 篇 土木工程
- 33 篇 力学（可授工学、理...
- 31 篇 航空宇航科学与技...
- 26 篇 安全科学与工程
- 20 篇 材料科学与工程（可...
- 20 篇 交通运输工程
3,409 篇 医学
- 3,408 篇 临床医学
1,980 篇 理学
- 1,006 篇 数学
- 973 篇 物理学
- 359 篇 统计学（可授理学、...
- 336 篇 生物学
- 231 篇 系统科学
- 24 篇 化学
258 篇 管理学
- 138 篇 管理科学与工程(可...
- 122 篇 图书情报与档案管...
- 27 篇 工商管理
19 篇 法学
- 19 篇 社会学
14 篇 农学
8 篇 教育学
7 篇 经济学
3 篇 军事学
3 篇 艺术学

主题

7,893 篇 computer vision
2,727 篇 training
2,680 篇 pattern recognit...
1,760 篇 computational mo...
1,644 篇 visualization
1,410 篇 cameras
1,372 篇 three-dimensiona...
1,327 篇 shape
1,213 篇 face recognition
1,207 篇 image segmentati...
1,164 篇 feature extracti...
1,109 篇 robustness
1,087 篇 semantics
983 篇 layout
959 篇 object detection
949 篇 computer archite...
942 篇 benchmark testin...
931 篇 codes
902 篇 computer science
859 篇 deep learning

机构

174 篇 univ sci & techn...
161 篇 carnegie mellon ...
148 篇 univ chinese aca...
144 篇 chinese univ hon...
110 篇 microsoft resear...
106 篇 tsinghua univ pe...
103 篇 zhejiang univ pe...
99 篇 swiss fed inst t...
92 篇 tsinghua univers...
89 篇 microsoft res as...
88 篇 shanghai ai lab ...
81 篇 zhejiang univers...
76 篇 alibaba grp peop...
73 篇 university of sc...
73 篇 hong kong univ s...
72 篇 peking univ peop...
72 篇 university of ch...
68 篇 shanghai jiao to...
66 篇 univ oxford oxfo...
66 篇 shanghai jiao to...

作者

79 篇 van gool luc
70 篇 zhang lei
59 篇 timofte radu
48 篇 yang yi
47 篇 xiaoou tang
45 篇 luc van gool
43 篇 darrell trevor
43 篇 tian qi
42 篇 loy chen change
42 篇 sun jian
42 篇 li fei-fei
40 篇 qi tian
38 篇 li stan z.
36 篇 chen xilin
36 篇 torralba antonio
35 篇 vasconcelos nuno
35 篇 shan shiguang
35 篇 liu yang
34 篇 liu xiaoming
34 篇 tao dacheng

语言

19,484 篇 英文
2 篇 日文
2 篇 中文
1 篇 其他

检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2000"

共 19489 条记录，以下是4821-4830 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：

DeshadowNet: A Multi-context Embedding Deep Network for Shadow Removal 30

DeshadowNet: A Multi-context Embedding Deep Network for Shad...

引用

30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Qu, Liangqiong Tian, Jiandong He, Shengfeng Tang, Yandong Lau, Rynson W. H. Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China City Univ Hong Kong Hong Kong Hong Kong Peoples R China South China Univ Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538604571

Shadow removal is a challenging task as it requires the detection/annotation of shadows as well as semantic understanding of the scene. In this paper, we propose an automatic and end-to-end deep neural network (DeshadowNet) to tackle these problems in a unified manner. DeshadowNet is designed with a multi-context architecture, where the output shadow matte is predicted by embedding information from three different perspectives. The first global network extracts shadow features from a global view. Two levels of features are derived from the global network and transferred to two parallel networks. While one extracts the appearance of the input image, the other one involves semantic understanding for final prediction. These two complementary networks generate multi-context features to obtain the shadow matte with fine local details. To evaluate the performance of the proposed method, we construct the first large scale benchmark with 3088 image pairs. Extensive experiments on two publicly available benchmarks and our large-scale benchmark show that the proposed method performs favorably against several state-of-the-art methods.

关键词： Semantics Feature extraction Lighting Image color analysis Benchmark testing computer vision Benchmark testing image colour analysis shadows Semantics images computer vision Lighting Feature extraction Online resources parallel network semiotics BENCHMARKS embedding

来源：评论

学校读者我要写书评

暂无评论

3DAC: Learning Attribute Compression for Point Clouds

3DAC: Learning Attribute Compression for Point Clouds

引用

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

作者： Fang, Guangchi Hu, Qingyong Wang, Hanyun Xu, Yiling Guo, Yulan Sun Yat Sen Univ Shenzhen Campus Guangzhou Peoples R China Univ Oxford Oxford England Informat Engn Univ Zhengzhou Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Natl Univ Def Technol Changsha Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We study the problem of attribute compression for large-scale unstructured 3D point clouds. Through an in-depth exploration of the relationships between different encoding steps and different attribute channels, we introduce a deep compression network, termed 3DAC, to explicitly compress the attributes of 3D point clouds and reduce storage usage in this paper. Specifically, the point cloud attributes such as color and reflectance are firstly converted to transform coefficients. We then propose a deep entropy model to model the probabilities of these coefficients by considering information hidden in attribute transforms and previous encoded attributes. Finally, the estimated probabilities are used to further compress these transform coefficients to a final attributes bitstream. Extensive experiments conducted on both indoor and outdoor large-scale open point cloud datasets, including ScanNet and SemanticKITTI, demonstrated the superior compression rates and reconstruction quality of the proposed method.

关键词： Point cloud compression Reflectivity computer vision Three-dimensional displays Correlation Transforms Entropy

来源：评论

学校读者我要写书评

暂无评论

Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration 32

Dual Residual Networks Leveraging the Potential of Paired Op...

引用

32nd ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Liu, Xing Suganuma, Masanori Sun, Zhun Okatani, Takayuki Tohoku Univ Grad Sch Informat Sci Sendai Miyagi Japan RIKEN Ctr AIP Tokyo Japan

ISBN: (纸本)9781728132938

In this paper, we study design of deep neural networks for tasks of image restoration. We propose a novel style of residual connections dubbed "dual residual connection", which exploits the potential of pairedoperations, e.g., upand down-sampling or convolution with large- and small-size kernels. We design a modular block implementing this connection style;it is equipped with two containers to which arbitrary paired operations are inserted. Adopting the "unraveled" view of the residual networks proposed by Veit et al., we point out that a stack of the proposed modular blocks allows the first operation in a block interact with the second operation in any subsequent blocks. Specifying the two operations in each of the stacked blocks, we build a complete network for each individual task of image restoration. We experimentally evaluate the proposed approach on five image restoration tasks using nine datasets. The results show that the proposed networks with properly chosen paired operations outperform previous methods on almost all of the tasks and datasets.

关键词： Computational Photography Deep Learning Image and Video Synthesis Low-level vision vision Applications and Syst

来源：评论

学校读者我要写书评

暂无评论

KNN Local Attention for Image Restoration

KNN Local Attention for Image Restoration

引用

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

作者： Lee, Hunsang Choi, Hyesong Sohn, Kwanghoon Min, Dongbo Yonsei Univ Seoul South Korea Ewha W Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent works attempt to integrate the non-local operation with CNNs or Transformer, achieving remarkable performance in image restoration tasks. The global similarity, however, has the problems of the lack of locality and the high computational complexity that is quadratic to an input resolution. The local attention mechanism alleviates these issues by introducing the inductive bias of the locality with convolution-like operators. However, by focusing only on adjacent positions, the local attention suffers from an insufficient receptive field for image restoration. In this paper, we propose a new attention mechanism for image restoration, called k-NN Image Transformer (KiT), that rectifies the above mentioned limitations. Specifically, the KiT groups k-nearest neighbor patches with locality sensitive hashing (LSH), and the grouped patches are aggregated into each query patch by performing a pair-wise local attention. In this way, the pair-wise operation establishes non-local connectivity while maintaining the desired properties of the local attention, i.e., inductive bias of locality and linear complexity to input resolution. The proposed method outperforms state-of-the-art restoration approaches on image denoising, deblurring and deraining benchmarks. The code will be available soon.

关键词： Image color analysis Focusing Machine learning Transformers Image restoration pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

Exposure Normalization and Compensation for Multiple-Exposure Correction

Exposure Normalization and Compensation for Multiple-Exposur...

引用

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

作者： Huang, Jie Liu, Yajing Fu, Xueyang Zhou, Man Wang, Yang Zhao, Feng Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Images captured with improper exposures usually bring unsatisfactory visual effects. Previous works mainly focus on either underexposure or overexposure correction, resulting in poor generalization to various exposures. An alternative solution is to mix the multiple exposure data for training a single network. However, the procedures of correcting underexposure and overexposure to normal exposures are much different from each other, leading to large discrepancies for the network in correcting multiple-exposures, thus resulting in poor performance. The key point to address this issue lies in bridging different exposure representations. To achieve this goal, we design a multiple exposure correction framework based on an Exposure Normalization and Compensation (ENC) module. Specifically, the ENC module consists of an exposure normalization part for mapping different exposure features to the exposure-invariant feature space, and a compensation part for integrating the initial features unprocessed by the exposure normalization part to ensure the completeness of information. Besides, to further alleviate the imbalanced performance caused by variations in the optimization process, we introduce a parameter regularization fine-tuning strategy to improve the performance of the worst-performed exposure without degrading other exposures. Our model empowered by ENC outperforms the existing methods by more than 2dB and is robust to multiple image enhancement tasks, demonstrating its effectiveness and generalization capability for real-world applications. Code: https://***/KevinJ-Huang/ExposureNorm-Compensation.

关键词： Training computer vision Codes Computational modeling Visual effects pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

Towards Unsupervised Domain Generalization

Towards Unsupervised Domain Generalization

引用

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

作者： Zhang, Xingxuan Zhou, Linjun Xu, Renzhe Cui, Peng Shen, Zheyan Liu, Haoxin Tsinghua Univ Dept Comp Sci Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Domain generalization (DG) aims to help models trained on a set of source domains generalize better on unseen target domains. The performances of current DG methods largely rely on sufficient labeled data, which are usually costly or unavailable, however. Since unlabeled data are far more accessible, we seek to explore how unsupervised learning can help deep models generalize across domains. Specifically, we study a novel generalization problem called unsupervised domain generalization (UDG), which aims to learn generalizable models with unlabeled data and analyze the effects of pre-training on DG. In UDG, models are pre-trained with unlabeled data from various source domains before being trained on labeled source data and eventually tested on unseen target domains. Then we propose a method named Domain-Aware Representation LearnING (DARLING) to cope with the significant and misleading heterogeneity within unlabeled pretraining data and severe distribution shifts between source and target data. Surprisingly we observe that DARLING can not only counterbalance the scarcity of labeled data but also further strengthen the generalization ability of models when the labeled data are insufficient. As a pretraining approach, DARLING shows superior or comparable performance compared with ImageNet pretraining protocol even when the available data are unlabeled and of a vastly smaller amount compared to ImageNet, which may shed light on improving generalization with large-scale unlabeled data.

关键词： Representation learning Analytical models computer vision Protocols Computational modeling Data models pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Classification-Then-Grounding: Reformulating Video Scene Graphs as Temporal Bipartite Graphs

Classification-Then-Grounding: Reformulating Video Scene Gra...

引用

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

作者： Gao, Kaifeng Chen, Long Niu, Yulei Shao, Jian Xiao, Jun Zhejiang Univ Hangzhou Peoples R China Columbia Univ New York NY 10027 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Today's VidSGG models are all proposal-based methods, i.e., they first generate numerous paired subject-object snippets as proposals, and then conduct predicate classification for each proposal. In this paper, we argue that this prevalent proposal-based framework has three inherent drawbacks: 1) The ground-truth predicate labels for proposals are partially correct. 2) They break the high-order relations among different predicate instances of a same subject-object pair. 3) VidSGG performance is upper-bounded by the quality of the proposals. To this end, we propose a new classification-then-grounding framework for VidSGG, which can avoid all the three overlooked drawbacks. Meanwhile, under this framework, we reformulate the video scene graphs as temporal bipartite graphs, where the entities and predicates are two types of nodes with time slots, and the edges denote different semantic roles between these nodes. This formulation takes full advantage of our new framework. Accordingly, we further propose a novel Bipartite Graph based SGG model: BIG. It consists of a classification stage and a grounding stage, where the former aims to classify the categories of all the nodes and the edges, and the latter tries to localize the temporal location of each relation instance. Extensive ablations on two VidSGG datasets have attested to the effectiveness of our framework and BIG.

关键词： computer vision Image analysis Codes Grounding Semantics Bipartite graph pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Model Barrier: A Compact Un-Transferable Isolation Domain for Model Intellectual Property Protection

Model Barrier: A Compact Un-Transferable Isolation Domain fo...

引用

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

作者： Wang, Lianyu Wang, Meng Zhang, Daoqiang Fu, Huazhu Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Agcy Sci Technol & Res STAR IHPC Singapore 138632 Singapore

ISBN: (纸本)9798350301298

As scientific and technological advancements result from human intellectual labor and computational costs, protecting model intellectual property (IP) has become increasingly important to encourage model creators and owners. Model IP protection involves preventing the use of well-trained models on unauthorized domains. To address this issue, we propose a novel approach called Compact Un-Transferable Isolation Domain (CUTI-domain), which acts as a barrier to block illegal transfers from authorized to unauthorized domains. Specifically, CUTI-domain blocks cross-domain transfers by highlighting the private style features of the authorized domain, leading to recognition failure on unauthorized domains with irrelevant private style features. Moreover, we provide two solutions for using CUTI-domain depending on whether the unauthorized domain is known or not: target-specified CUTI-domain and target-free CUTI-domain. Our comprehensive experimental results on four digit datasets, CIFAR10 & STL10, and VisDA-2017 dataset demonstrate that CUTI-domain can be easily implemented as a plug-and-play module with different backbones, providing an efficient solution for model IP protection.

关键词： accountability ethics in vision fairness privacy Transparency

来源：评论

学校读者我要写书评

暂无评论

DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking

<i>DyGLIP</i>: A Dynamic Graph Model with Link Prediction fo...

引用

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

作者： Quach, Kha Gia Pha Nguyen Le, Huu Truong, Thanh-Dat Duong, Chi Nhan Minh-Triet Tran Luu, Khoa Concordia Univ Montreal PQ Canada VinAI Res Hanoi Vietnam Chalmers Univ Technol Gothenburg Sweden Univ Arkansas Fayetteville AR 72701 USA VNU HCM Univ Sci Ho Chi Minh City Vietnam

ISBN: (纸本)9781665445092

Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications. Despite a large number of existing works, solving the data association problem in any MC-MOT pipeline is arguably one of the most challenging tasks. Developing a robust MC-MOT system, however, is still highly challenging due to many practical issues such as inconsistent lighting conditions, varying object movement patterns, or the trajectory occlusions of the objects between the cameras. To address these problems, this work, therefore, proposes a new Dynamic Graph Model with Link Prediction (DyGLIP) approach 1 to solve the data association task. Compared to existing methods, our new model offers several advantages, including better feature representations and the ability to recover from lost tracks during camera transitions. Moreover, our model works gracefully regardless of the overlapping ratios between the cameras. Experimental results show that we outperform existing MC-MOT algorithms by a large margin on several practical datasets. Notably, our model works favorably on online settings but can be extended to an incremental approach for large-scale datasets.

关键词： computer vision Pipelines Predictive models Cameras Prediction algorithms Robustness Trajectory

来源：评论

学校读者我要写书评

暂无评论

DGECN: A Depth-Guided Edge Convolutional Network for End-to-End 6D Pose Estimation

DGECN: A Depth-Guided Edge Convolutional Network for End-to-...

引用

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

作者： Cao, Tuo Luo, Fei Fu, Yanping Zhang, Wenxiao Zheng, Shengjie Xiao, Chunxia Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei Anhui Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Monocular 6D pose estimation is a fundamental task in computer vision. Existing works often adopt a two-stage pipeline by establishing correspondences and utilizing a RANSAC algorithm to calculate 6 degrees-of-freedom (6DoF) pose. Recent works try to integrate differentiable RANSAC algorithms to achieve an end-to-end 6D pose estimation. However, most of them hardly consider the geometric features in 3D space, and ignore the topology cues when performing differentiable RANSAC algorithms. To this end, we proposed a Depth-Guided Edge Convolutional Network (DGECN) for 6D pose estimation task. We have made efforts from the following three aspects: 1) We take advantages of estimated depth information to guide both the correspondences-extraction process and the cascaded differentiable RANSAC algorithm with geometric information. 2) We leverage the uncertainty of the estimated depth map to improve accuracy and robustness of the output 6D pose. 3) We propose a differentiable Perspective-n-Point(PnP) algorithm via edge convolution to explore the topology relations between 2D-3D correspondences. Experiments demonstrate that our proposed network outperforms current works on both effectiveness and efficiency.

关键词： computer vision Uncertainty Three-dimensional displays Network topology Pose estimation Pipelines Robustness

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 << < 479 480 481 482 483 484 485 486 487 488 > >>

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：