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

8,902 篇 会议
42 篇 期刊文献
18 册 图书

馆藏范围

8,961 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

4,560 篇 工学
- 4,019 篇 计算机科学与技术...
- 2,178 篇 软件工程
- 1,242 篇 光学工程
- 555 篇 控制科学与工程
- 431 篇 信息与通信工程
- 429 篇 机械工程
- 294 篇 电气工程
- 287 篇 仪器科学与技术
- 179 篇 生物工程
- 159 篇 生物医学工程（可授...
- 119 篇 电子科学与技术（可...
- 61 篇 安全科学与工程
- 58 篇 建筑学
- 58 篇 化学工程与技术
- 52 篇 土木工程
- 49 篇 交通运输工程
- 40 篇 力学（可授工学、理...
2,065 篇 理学
- 1,382 篇 物理学
- 1,198 篇 数学
- 420 篇 统计学（可授理学、...
- 238 篇 生物学
- 54 篇 化学
- 36 篇 系统科学
263 篇 管理学
- 180 篇 图书情报与档案管...
- 89 篇 管理科学与工程(可...
- 47 篇 工商管理
222 篇 医学
- 221 篇 临床医学
- 39 篇 基础医学(可授医学...
205 篇 艺术学
- 205 篇 设计学（可授艺术学...
45 篇 法学
- 43 篇 社会学
21 篇 农学
14 篇 教育学
9 篇 经济学
6 篇 军事学

主题

3,413 篇 computer vision
1,216 篇 pattern recognit...
946 篇 cameras
908 篇 conferences
765 篇 computer science
674 篇 image segmentati...
618 篇 layout
598 篇 training
548 篇 shape
518 篇 robustness
451 篇 feature extracti...
448 篇 humans
445 篇 face recognition
405 篇 computational mo...
402 篇 object detection
365 篇 visualization
356 篇 computer archite...
336 篇 application soft...
304 篇 lighting
258 篇 image reconstruc...

机构

41 篇 microsoft resear...
30 篇 department of co...
25 篇 department of co...
23 篇 institute for co...
22 篇 department of co...
22 篇 school of comput...
20 篇 university of sc...
20 篇 swiss fed inst t...
19 篇 tsinghua univers...
19 篇 institute of com...
18 篇 swiss fed inst t...
17 篇 the robotics ins...
17 篇 carnegie mellon ...
17 篇 computer vision ...
17 篇 department of co...
16 篇 institute of inf...
16 篇 school of comput...
15 篇 school of comput...
15 篇 carnegie mellon ...
14 篇 national laborat...

作者

57 篇 timofte radu
25 篇 huang thomas s.
24 篇 van gool luc
23 篇 s.k. nayar
22 篇 nayar shree k.
22 篇 t. kanade
21 篇 jain anil k.
20 篇 luc van gool
19 篇 t.s. huang
18 篇 xiaoou tang
18 篇 murino vittorio
18 篇 horst bischof
17 篇 a.k. jain
17 篇 t. darrell
16 篇 g. healey
16 篇 bowyer kevin w.
16 篇 bischof horst
15 篇 m.j. black
15 篇 li stan z.
15 篇 m. shah

语言

8,913 篇 英文
40 篇 其他
8 篇 中文
1 篇 土耳其文

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

共 8962 条记录，以下是1811-1820 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：

Unpaired Real-World Super-Resolution with Pseudo Controllable Restoration

Unpaired Real-World Super-Resolution with Pseudo Controllabl...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： André s Romero Luc Van Gool Radu Timofte Computer Vision Lab ETH Z&#x00FC rich KU Leuven University of W&#x00FC rzburg

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Current super-resolution methods rely on the bicubic down-sampling assumption in order to develop the ill-posed reconstruction of the low-resolution image. Not surprisingly, these approaches fail when using real-world low-resolution images due to the presence of artifacts and intrinsic noise absent in the bicubic setup. Consequently, attention is increasingly paid to techniques that alleviate this problem and super-resolve real-world images. As acquiring paired real-world datasets is a challenging problem, real-world super-resolution solutions are traditionally tackled as a blind problem or as an unpaired data-driven problem. The former makes assumptions about the downsampling operations, the latter uses unpaired training to learn the real distributions. Recently, blind approaches have dominated this problem by assuming a diverse bank of degradations, whereas the unpaired solutions have shown under-performance due to the two-staged training. In this paper, we propose an unpaired real-world super-resolution method that performs on par, or even better than blind paired approaches by introducing a pseudo-controllable restoration module in a fully end-to-end system.

关键词： Training Degradation computer vision conferences Superresolution Image restoration pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Reconstruct, Rasterize and Backprop: Dense shape and pose estimation from a single image

Reconstruct, Rasterize and Backprop: Dense shape and pose es...

引用

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

作者： Pokale, Aniket Aggarwal, Aditya Jatavallabhula, Krishna Murthy Krishna, K. Madhava IIIT Hyderabad Robot Res Ctr KCIS Hyderabad India Univ Montreal Mila Montreal PQ Canada

ISBN: (纸本)9781728193601

This paper presents a new system to obtain dense object reconstructions along with 6-DoF poses from a single image. Geared towards high fidelity reconstruction, several recent approaches leverage implicit surface representations and deep neural networks to estimate a 3D mesh of an object, given a single image. However, all such approaches recover only the shape of an object;the reconstruction is often in a canonical frame, unsuitable for downstream robotics tasks. To this end, we leverage recent advances in differentiable rendering (in particular, rasterization) to close the loop with 3D reconstruction in camera frame. We demonstrate that our approach-dubbed reconstruct, rasterize and backprop (RRB)-achieves significantly lower pose estimation errors compared to prior art, and is able to recover dense object shapes and poses from imagery. We further extend our results to an (offline) setup, where we demonstrate a dense monocular object-centric egomotion estimation system.

关键词： Three dimensional computer graphics

来源：评论

学校读者我要写书评

暂无评论

Unpaired Faces to Cartoons: Improving XGAN

Unpaired Faces to Cartoons: Improving XGAN

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Stev H. Ramos Joel Cabrera Daniel Ibá ñ ez Alejandro B. Jimé nez-Panta Cé sar Beltrá n-Castañ ó n Edwin Villanueva Pontifical Catholic University of Peru Lima Peru

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Domain Adaptation is a task that aims to translate an image from a source domain to a desired target domain. Current methods in domain adaptation use adversarial training based on Generative Adversarial Networks (GAN). In the present work, we focus on the task of domain adaptation from real faces to cartoon face images. We start from a baseline architecture called XGAN and introduce some improvements to it. Our proposed model is called W-XDGAN, which uses a form of GAN called Wasserstein-GAN, learns to approximate the Wasserstein Distance, and adds a denoiser to smooth the output cartoons. Whereas the original XGAN paper only presented a qualitative analysis, the advantages of this solution are demonstrated both quantitatively and qualitatively by comparing the results with models such as UNIT and original XGAN. Our code and models are publicly available at https://***/IAmigos/avatar-image-generator.

关键词： Training computer vision Codes Computational modeling conferences computer architecture Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing

Domain Agnostic Feature Learning for Image and Video Based F...

引用

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

作者： Saha, Suman Xu, Wenhao Kanakis, Menelaos Georgoulis, Stamatios Chen, Yuhua Paudel, Danda Pani Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Nowadays, the increasingly growing number of mobile and computing devices has led to a demand for safer user authentication systems. Face anti-spoofing is a measure towards this direction for biometric user authentication, and in particular face recognition, that tries to prevent spoof attacks. The state-of-the-art anti-spoofing techniques leverage the ability of deep neural networks to learn discriminative features, based on cues from the training set images or video samples, in an effort to detect spoof attacks. However, due to the particular nature of the problem, i.e. large variability due to factors like different backgrounds, lighting conditions, camera resolutions, spoof materials, etc., these techniques typically fail to generalize to new samples. In this paper, we explicitly tackle this problem and propose a class-conditional domain discriminator module, that, coupled with a gradient reversal layer, tries to generate live and spoof features that are discriminative, but at the same time robust against the aforementioned variability factors. Extensive experimental analysis shows the effectiveness of the proposed method over existing image- and video-based anti-spoofing techniques, both in terms of numerical improvement as well as when visualizing the learned features.

关键词： Face Lighting computer vision Authentication Feature extraction Cameras Visualization

来源：评论

学校读者我要写书评

暂无评论

Improving Multi-Target Multi-Camera Tracking by Track Refinement and Completion

Improving Multi-Target Multi-Camera Tracking by Track Refine...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Andreas Specker Lucas Florin Mickael Cormier Jü rgen Beyerer Karlsruhe Institute of Technology Fraunhofer IOSB Fraunhofer Center for Machine Learning

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Multi-camera tracking of vehicles on a city-wide level is a core component of modern traffic monitoring systems. For this task, single-camera tracking failures are the most common causes of errors concerning automatic multi-target multi-camera tracking systems. To address these problems, we propose several modules that aim at improving single-camera tracklets, e.g., appearance-based tracklet splitting, single-camera clustering, and track completion. After these track refinement steps, hierarchical clustering is used to associate the enhanced single-camera tracklets. During this stage, we leverage vehicle re-identification features as well as prior knowledge about the scene's topology. Last, the proposed track completion strategy is adopted for the cross-camera association task to obtain the final multi-camera tracks. Our method proves itself competitive: With it, we achieved 4th place in track 1 of the 2022 AI City Challenge.

关键词： computer vision conferences Urban areas Topology pattern recognition Task analysis Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Investigating the Role of Image Processing to Identify pattern for Industrial Automation 11

Investigating the Role of Image Processing to Identify Patte...

引用

11th International conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Kaur, Gagandeep Balu, Vincent Chandigarh School of Business Department of Master of Computer Application Jhanjeri India Sanskriti University Department of Mechanical Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9781665487320

In situations when both the output and the result are graphical, classification is used. The science's name was decided upon because of its concentration on image analysis. Imaging, satellite data, contrasts amplification, and voice signal production all could be done on the architecture here. A image or video cannot be recognized until a certain component or feature is found. In order to spot patterns, Cad system is used in this study. An area of study was the function of image processing in quality assurance. Even at high speeds., excellent ray tracing enables faultless record. Corporate machine learning is often used in process automation. computer tech vision equipment includes things like camera-activating displays and movable video recorded tracks. © 2022 ieee.

关键词： Image recognition

来源：评论

学校读者我要写书评

暂无评论

An Analytical Framework for Trusted Machine Learning and computer vision Running with Blockchain

An Analytical Framework for Trusted Machine Learning and Com...

引用

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

作者： Wang, Tao Du, Maggie Wu, Xinmin He, Taiping SAS Inst Inc Cary NC 27513 USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Machine learning algorithms often use data from databases that are mutable;therefore, the data and the results of machine learning cannot be fully trusted. Also, the learning process is often difficult to automate. A unified analytical framework for trusted machine learning has been presented in the literature to address both issues. It is proposed building a trusted machine learning system by using blockchain technology, which can store data in a permanent and immutable way. In addition, smart contracts on blockchain are used to automate the machine learning process. However, in such a blockchain framework, data efficiency is a big concern, because it is very expensive to store a large amount of data on blockchain. On the other hand, machine learning based computer vision systems often rely on a lot of data. Therefore, to fully leverage a blockchain-based machine learning framework for computer vision systems, data efficiency issues must be addressed. This paper investigates how to enhance data efficiency in such a framework to bring computer vision systems to the edge. It presents a three-step approach. First, a lightweight machine learning model is trained on the server layer. Second, the trained model is saved in a special binary data format for data efficiency. Finally, the streaming layer takes these binary data as input and scores incoming new data in an online fashion. Real-time semantic segmentation for autonomous driving is used as an example to demonstrate how this approach works. This paper makes the following contributions. First, it improves the analytical framework for trusted computer vision systems based on blockchain. Second, the real-time semantic segmentation example shows how data-efficient learning for computer vision can be performed on the edge.

关键词： Machine learning computer vision Computational modeling Servers Contracts Automation

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised Learning of Pose-Informed Latents

Self-Supervised Learning of Pose-Informed Latents

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Raphaë l Jean Pierre-Luc St-Charles Sö ren Pirk Simon Brodeur Menya Solutions Mila Mila AMLRT Google Research

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Siamese network architectures trained for self-supervised instance recognition can learn powerful visual representations that are useful in various tasks. Many such approaches maximize the similarity between representations of augmented images of the same object. In this paper, we depart from traditional self-supervised learning benchmarks by defining a novel methodology for new challenging tasks such as zero shot pose estimation. Our goal is to show that common Siamese networks can effectively be trained on frame pairs from video sequences to generate pose-informed representations. Unlike parallel efforts that focus on introducing new image-space operators for data augmentation, we argue that extending the augmentation strategy by using different frames of a video leads to more powerful representations. To show the effectiveness of this approach, we use the Objectron and UCF101 datasets to learn representations and evaluate them on pose estimation, action recognition, and object re-identification. Furthermore, we carefully validate our method against a number of baselines.

关键词： Training computer vision Visualization Pose estimation Video sequences Self-supervised learning Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

An investigation of attention mechanisms in histopathology whole-slide-image analysis for regression objectives 18

An investigation of attention mechanisms in histopathology w...

引用

18th ieee/CVF International conference on computer vision (ICCV)

作者： Weitz, Philippe Wang, Yinxi Hartman, Johan Rantalainen, Mattias Karolinska Inst Solna Sweden

ISBN: (纸本)9781665401913

Analysis of whole-slide-images (WSIs) of histopathology tissue sections remains challenging due to the gigapixel scale of these images, which often necessitates their division into smaller image tiles. Recently, attention mechanisms have been successfully applied to alleviate the tile-to-slide challenges for classification tasks based on WSIs. In this study, we explore the potential of attention mechanisms in regression settings, by comparing four modelling approaches, two of which use attention mechanisms. We evaluate these models both in a simulated experiment using the MNIST data set, and in real histopathology data sets focused on prediction of gene expression levels from WSIs, including an analysis of the local prediction performance using spatial transcriptomics. The MNIST simulation demonstrates that if only a small proportion of instances in a set of images contribute to the set-level regression label, attention mechanisms may be preferable to commonly applied weakly supervised models. When predicting gene expression from WSIs, the differences in performance between the models that we investigated were small. Nevertheless, we found some evidence that attention mechanisms may be more sensitive to domain shifts. In the regression-based task of gene expression prediction, the prediction performance in the present study appears to be limited by other factors rather than by the choice of modelling approach. Nevertheless, attention mechanisms appear promising for regression objectives and warrant further investigation.

关键词： Analytical models computer vision Histopathology Computational modeling conferences Focusing Predictive models

来源：评论

学校读者我要写书评

暂无评论

Road Condition Detection and Crowdsourced Data Collection for Accident Prevention: A Deep Learning Approach 12

Road Condition Detection and Crowdsourced Data Collection fo...

引用

12th International conference on Image Processing Theory, Tools and Applications, IPTA 2023

作者： Jahan, Md Saroar Islam, Mominul Hossain, Md Sanjid Kabir Mim, Jhuma Oussalah, Mourad Akter, Nasrin Oulu Finland Daffodil International University Dept. of Cse Dhaka Bangladesh Lut Univerity Computer Vision and Pattern Recognition Lappeenranta Finland University of Oulu Cmvs Faculty of Itee Oulu Finland

ISBN: (纸本)9798350325416

Bangladesh is one of the countries struggling to prevent road accidents, which is a global cause for concern. An early warning system that indicates road conditions can contribute to the prevention task. For this purpose, a deep-learning based approach using a Convolutional Neural Network (CNN) to learn from random road images the safety factor is developed. This results in a three-class categorization: (i) Severely risky roads, (ii) Mildly risky roads, and (iii) Normal roads. The application of deep learning techniques in this study yields an accuracy of 95.5% in detecting problematic road conditions. Furthermore, based on the study's findings, a mobile application has been developed. The app enables real-time crowdsourced data collection of road conditions and provides a platform for users to share this information in real-time with other drivers, thereby, contributing to prevent accidents and raise awareness among drivers and users by pinpointing the location of the risky road. Finally, crowdsourced data has been reused to update the trained model, which further improves the classifier accuracy. © 2023 ieee.

关键词： Safety factor

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 << < 178 179 180 181 182 183 184 185 186 187 > >>

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：