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3021-3030 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：

Top-Down Networks: A coarse-to-fine reimagination of CNNs

Top-Down Networks: A coarse-to-fine reimagination of CNNs

引用

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

作者： Ioannis Lelekas Nergis Tomen Silvia L. Pintea Jan C. van Gemert Computer Vision Lab Delft University of Technology Netherlands

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Biological vision adopts a coarse-to-fine information processing pathway, from initial visual detection and binding of salient features of a visual scene, to the enhanced and preferential processing given relevant stimuli. On the contrary, CNNs employ a fine-to-coarse processing, moving from local, edge-detecting filters to more global ones extracting abstract representations of the input. In this paper we reverse the feature extraction part of standard bottom-up architectures and turn them upside-down: We propose top-down networks. Our proposed coarse-to-fine pathway, by blurring higher frequency information and restoring it only at later stages, offers a line of defence against adversarial attacks that introduce high frequency noise. Moreover, since we increase image resolution with depth, the high resolution of the feature map in the final convolutional layer contributes to the explainability of the network's decision making process. This favors object-driven decisions over context driven ones, and thus provides better localized class activation maps. This paper offers empirical evidence for the applicability of the top-down resolution processing to various existing architectures on multiple visual tasks.

关键词： Feature extraction Spatial resolution computer architecture Merging Task analysis Visualization Robustness

来源：评论

学校读者我要写书评

暂无评论

Bilinear Parameterization For Differentiable Rank-Regularization

Bilinear Parameterization For Differentiable Rank-Regulariza...

引用

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

作者： Marcus Valtonen Örnhag Carl Olsson Anders Heyden Centre for Mathematical Sciences Lund University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Low rank approximation is a commonly occurring problem in many computer vision and machine learning applications. There are two common ways of optimizing the resulting models. Either the set of matrices with a given rank can be explicitly parametrized using a bilinear factorization, or low rank can be implicitly enforced using regularization terms penalizing non-zero singular values. While the former approach results in differentiable problems that can be efficiently optimized using local quadratic approximation, the latter is typically not differentiable (sometimes even discontinuous) and requires first order subgradient or splitting methods. It is well known that gradient based methods exhibit slow convergence for ill-conditioned problems. In this paper we show how many non-differentiable regularization methods can be reformulated into smooth objectives using bilinear parameterization. This allows us to use standard second order methods, such as Levenberg- Marquardt (LM) and Variable Projection (VarPro), to achieve accurate solutions for ill-conditioned cases. We show on several real and synthetic experiments that our second order formulation converges to substantially more accurate solutions than competing state-of-the-art methods. 1

关键词： Convergence computer vision Structure from motion Robustness Optimization Standards Deformable models

来源：评论

学校读者我要写书评

暂无评论

Homogeneous Linear Inequality Constraints for Neural Network Activations

Homogeneous Linear Inequality Constraints for Neural Network...

引用

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

作者： Thomas Frerix Matthias Nießner Daniel Cremers Technical University of Munich

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We propose a method to impose homogeneous linear inequality constraints of the form Ax ≤ 0 on neural network activations. The proposed method allows a data-driven training approach to be combined with modeling prior knowledge about the task. One way to achieve this task is by means of a projection step at test time after unconstrained training. However, this is an expensive operation. By directly incorporating the constraints into the architecture, we can significantly speed-up inference at test time; for instance, our experiments show a speed-up of up to two orders of magnitude over a projection method. Our algorithm computes a suitable parameterization of the feasible set at initialization and uses standard variants of stochastic gradient descent to find solutions to the constrained network. Thus, the modeling constraints are always satisfied during training. Crucially, our approach avoids to solve an optimization problem at each training step or to manually trade-off data and constraint fidelity with additional hyperparameters. We consider constrained generative modeling as an important application domain and experimentally demonstrate the proposed method by constraining a variational autoencoder.

关键词： Training Neural networks Computational modeling Task analysis Optimization Machine learning computer architecture

来源：评论

学校读者我要写书评

暂无评论

Multi-object Graph-based Segmentation with Non-overlapping Surfaces

Multi-object Graph-based Segmentation with Non-overlapping S...

引用

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

作者： Patrick M. Jensen Anders B. Dahl Vedrana A. Dahl Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs Lyngby Denmark

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

For 3D images, segmentation via fitting surface meshes to object boundaries provides an efficient way to handle large images and enforce geometric prior knowledge. Furthermore, fitting such meshes with graph cuts has proven to be a versatile and robust framework. However, when segmenting multiple distinct objects in one image, current methods do not allow the natural constraint that objects should not overlap. In this paper, we present an extension to graph cut based methods which can provide a globally optimal segmentation of thousands of objects while guaranteeing no overlap. Our method works by separating objects with planes whose positions are determined as part of the graph cut. To demonstrate the general applicability of our method, we apply it to several 3D microscopy data sets from both biology and materials science. Our results show both quantitative and qualitative improvements.

关键词： Image segmentation Three-dimensional displays Microscopy Optical microscopy Shape Image edge detection Optimization

来源：评论

学校读者我要写书评

暂无评论

Evaluating Scalable Bayesian Deep Learning Methods for Robust computer vision

Evaluating Scalable Bayesian Deep Learning Methods for Robus...

引用

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

作者： Fredrik K. Gustafsson Martin Danelljan Thomas B. Schon Department of Information Technology Uppsala University Sweden Computer Vision Lab ETH Zurich Switzerland

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

While deep neural networks have become the go-to approach in computer vision, the vast majority of these models fail to properly capture the uncertainty inherent in their predictions. Estimating this predictive uncertainty can be crucial, for example in automotive applications. In Bayesian deep learning, predictive uncertainty is commonly decomposed into the distinct types of aleatoric and epistemic uncertainty. The former can be estimated by letting a neural network output the parameters of a certain probability distribution. Epistemic uncertainty estimation is a more challenging problem, and while different scalable methods recently have emerged, no extensive comparison has been performed in a real-world setting. We therefore accept this task and propose a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning. Our proposed framework is specifically designed to test the robustness required in real-world computer vision applications. We also apply this framework to provide the first properly extensive and conclusive comparison of the two current state-of-the- art scalable methods: ensembling and MC-dropout. Our comparison demonstrates that ensembling consistently provides more reliable and practically useful uncertainty estimates. Code is available at https://***/fregu856/evaluating_bdl.

关键词： Uncertainty Task analysis Estimation Predictive models computer vision Bayes methods Machine learning

来源：评论

学校读者我要写书评

暂无评论

An OCR for Classical Indic Documents Containing Arbitrarily Long Words

An OCR for Classical Indic Documents Containing Arbitrarily ...

引用

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

作者： Agam Dwivedi Rohit Saluja Ravi Kiran Sarvadevabhatla Centre For Visual Information Technology (CVIT) International Institute of Information Technology Hyderabad (IIIT-H) Hyderabad INDIA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

OCR for printed classical Indic documents written in Sanskrit is a challenging research problem. It involves complexities such as image degradation, lack of datasets and long-length words. Due to these challenges, the word accuracy of available OCR systems, both academic and industrial, is not very high for such documents. To address these shortcomings, we develop a Sanskrit specific OCR system. We present an attention-based LSTM model for reading Sanskrit characters in line images. We introduce a dataset of Sanskrit document images annotated at line level. To augment real data and enable high performance for our OCR, we also generate synthetic data via curated font selection and rendering designed to incorporate crucial glyph substitution rules. Consequently, our OCR achieves a word error rate of 15.97% and a character error rate of 3.71% on challenging Indic document texts and outperforms strong baselines. Overall, our contributions set the stage for application of OCRs on large corpora of classic Sanskrit texts containing arbitrarily long and highly conjoined words.

关键词： Optical character recognition software Training Error analysis Rendering (computer graphics) Decoding Character recognition Bidirectional control

来源：评论

学校读者我要写书评

暂无评论

A Multi-Level Supervision Model: A novel approach for Thermal Image Super Resolution

A Multi-Level Supervision Model: A novel approach for Therma...

引用

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

作者： Priya Kansal Sabari Nathan Couger Inc Shibuya Japan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

This paper proposes a novel architecture for thermal image super-resolution. A very large dataset is provided by PBVS 2020 in their super-resolution challenge. This dataset contains the images with three different resolution scales(low, medium, high) [1]. This dataset is used to train the proposed architecture to generate the super-resolution images in x2, x3, x4 scales. The proposed architecture is based on the residual blocks as the base units of the network. Along with this, the coordinate convolution layer and the convolutional block attention Module (CBAM) are also used in the architecture. Further, the multi-level supervision is implemented to supervise the output image resolution similarity with the real image at each block during training. To test the robustness of the proposed model, we evaluated our model on the Thermal-6 dataset [13]. The results show that our model is efficient to achieve the state of art results on the PBVS'2020 dataset. Further the results on the Thermal-6 dataset show that the model has a decent generalization capacity.

关键词： Spatial resolution Training Task analysis computer architecture Image edge detection Convolution

来源：评论

学校读者我要写书评

暂无评论

A Large Dataset of Historical Japanese Documents with Complex Layouts

A Large Dataset of Historical Japanese Documents with Comple...

引用

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

作者： Zejiang Shen Kaixuan Zhang Melissa Dell Harvard University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Deep learning-based approaches for automatic document layout analysis and content extraction have the potential to unlock rich information trapped in historical documents on a large scale. One major hurdle is the lack of large datasets for training robust models. In particular, little training data exist for Asian languages. To this end, we present HJDataset, a Large Dataset of Historical Japanese Documents with Complex Layouts. It contains over 250,000 layout element annotations of seven types. In addition to bounding boxes and masks of the content regions, it also includes the hierarchical structures and reading orders for layout elements. The dataset is constructed using a combination of human and machine efforts. A semi-rule based method is developed to extract the layout elements, and the results are checked by human inspectors. The resulting large-scale dataset is used to provide baseline performance analyses for text region detection using state-of-the-art deep learning models. And we demonstrate the usefulness of the dataset on real-world document digitization tasks. The dataset is available at https://***/HJDataset/.

关键词： Layout Indexes Robustness Task analysis ieee Regions Detectors Image color analysis

来源：评论

学校读者我要写书评

暂无评论

Dynamic Attention-based Visual Odometry

Dynamic Attention-based Visual Odometry

引用

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

作者： Xin-Yu Kuo Chien Liu Kai-Chen Lin Chun-Yi Lee Elsa Lab Department of Computer Science National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

This paper proposes a dynamic attention-based visual odometry framework (DAVO), a learning-based VO method, for estimating the ego-motion of a monocular camera. DAVO dynamically adjusts the attention weights on different semantic categories for different motion scenarios based on optical flow maps. These weighted semantic categories can then be used to generate attention maps that highlight the relative importance of different semantic regions in input frames for pose estimation. In order to examine the proposed DAVO, we perform a number of experiments on the KITTI Visual Odometry and SLAM benchmark suite to quantitatively and qualitatively inspect the impacts of the dynamically adjusted weights on the accuracy of the evaluated trajectories. Moreover, we design a set of ablation analyses to justify each of our design choices, and validate the effectiveness as well as the advantages of DAVO. Our experiments on the KITTI dataset shows that the proposed DAVO framework does provide satisfactory performance in ego-motion estimation, and is able deliver competitive performance when compared to the contemporary VO methods.

关键词： Semantics Cameras Trajectory Pose estimation Visual odometry Dynamics

来源：评论

学校读者我要写书评

暂无评论

Capturing Cellular Topology in Multi-Gigapixel Pathology Images

Capturing Cellular Topology in Multi-Gigapixel Pathology Ima...

引用

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

作者： Wenqi Lu Simon Graham Mohsin Bilal Nasir Rajpoot Fayyaz Minhas Department of Computer Science University of Warwick UK

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In computational pathology, multi-gigapixel whole slide images (WSIs) are typically divided into small patches because of their extremely large size and memory requirements. However, following this strategy, one risks losing visual context which is very important in the development of machine learning models aimed at diagnostic and prognostic assessment of WSIs. In this paper, we propose a novel graph convolutional neural network based model (called Slide Graph) which overcomes these limitations by building a graph representation of the cellular architecture in an entire WSI in a bottom-up manner. We evaluate Slide Graph for prediction of the status of human epidermal growth factor receptor 2 (HER2) and progesterone receptor (PR) expression from WSIs of H&E stained tissue slides of breast cancer. We demonstrate that the proposed model outperforms previous state-of-the-art methods and is more computationally efficient. The proposed paradigm of WSI-level graphs can potentially be applied to other problems in computational pathology as well.

关键词： Pathology Machine learning Visualization Computational modeling Convolutional neural networks Principal component analysis Breast cancer

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 << < 299 300 301 302 303 304 305 306 307 308 > >>

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：