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

3,915 篇 会议
2 篇 期刊文献

馆藏范围

3,917 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,011 篇 工学
- 2,919 篇 计算机科学与技术...
- 216 篇 软件工程
- 141 篇 机械工程
- 133 篇 光学工程
- 42 篇 生物工程
- 28 篇 信息与通信工程
- 25 篇 电气工程
- 17 篇 控制科学与工程
- 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
1,559 篇 医学
- 1,558 篇 临床医学
- 3 篇 基础医学(可授医学...
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
14 篇 管理学
- 7 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

2,408 篇 computer vision
1,085 篇 training
1,043 篇 pattern recognit...
805 篇 conferences
709 篇 computational mo...
543 篇 visualization
491 篇 computer archite...
446 篇 three-dimensiona...
410 篇 semantics
409 篇 benchmark testin...
383 篇 codes
331 篇 transformers
290 篇 deep learning
277 篇 feature extracti...
260 篇 neural networks
256 篇 task analysis
238 篇 shape
216 篇 image segmentati...
204 篇 measurement
202 篇 object detection

机构

72 篇 tsinghua univ pe...
59 篇 univ sci & techn...
55 篇 zhejiang univ pe...
55 篇 chinese univ hon...
51 篇 carnegie mellon ...
51 篇 peng cheng lab p...
49 篇 swiss fed inst t...
47 篇 sensetime res pe...
46 篇 shanghai ai lab ...
42 篇 univ hong kong p...
38 篇 huawei noahs ark...
35 篇 univ chinese aca...
35 篇 shanghai jiao to...
33 篇 alibaba grp peop...
32 篇 tech univ munich...
31 篇 stanford univ st...
30 篇 peking univ peop...
30 篇 swiss fed inst t...
29 篇 adobe res san jo...
29 篇 google res mount...

作者

63 篇 timofte radu
30 篇 van gool luc
19 篇 yang yi
19 篇 qiao yu
18 篇 loy chen change
16 篇 zhang lei
16 篇 radu timofte
15 篇 sun jian
14 篇 liu yang
14 篇 liu shuaicheng
14 篇 tao dacheng
13 篇 li xin
13 篇 fan haoqiang
13 篇 chen wei-ting
12 篇 luo ping
12 篇 chen dongdong
12 篇 wang kai
12 篇 wang xinchao
12 篇 torralba antonio
12 篇 ghanem bernard

语言

3,916 篇 英文
1 篇 其他

检索条件"任意字段=2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022"

共 3917 条记录，以下是3661-3670 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：

Deep coupling of random ferns 32

Deep coupling of random ferns

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Kim, Sangwon Jeong, Mira Lee, Deokwoo Ko, Byoung Chul Dept. of Computer Engineering Keimyung University Daegu42601 Korea Republic of

ISBN: (纸本)9781728125060

The purpose of this study is to design a new lightweight explainable deep model instead of deep neural networks (DNN) because of its high memory and processing resource requirement as well as black-box training although DNN is a powerful algorithm for classification and regression problems. This study propose a non-neural network style deep model based on combination of deep coupling random ferns (DCRF). In proposed DCRF, each neuron of a layer is replaced with the Fern and each layer consists of several type of Ferns. The proposed method showed a higher uniform performance in terms of the number of parameters and operations without a loss of accuracy compared to a few related studies including a DNN based model compression algorithm. © 2019 ieee computer Society. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Classification of computer generated and natural images based on efficient deep convolutional recurrent attention model 32

Classification of computer generated and natural images base...

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Tariang, Diangarti Bhalang Sengupta, Prithviraj Roy, Aniket Chakraborty, Rajat Subhra Naskar, Ruchira Indian Institute of Technology Kharagpur University of Illinois at Chicago Indian Statistical Institute Kolkata India National Instititute of Technology Rourkela India

ISBN: (纸本)9781728125060

Most state-of-the-art techniques of distinguishing natural images and computer generated images based on handcrafted feature and Convolutional Neural Network require processing of the entire input image pixels uniformly. As a result, such techniques usually require extensive computation time and memory, that scale linearly with the size of the input image in terms of number of pixels. In this paper, we deploy an efficient Deep Convolutional Recurrent Attention model with relatively less number of parameters, to distinguish between natural and computer generated images. The proposed model uses a glimpse network to locally process a sequence of selected image regions;hence, the number of parameters and computation time can be controlled effectively. We also adopt a local-to-global strategy by training image patches and classifying full-sized images using the simple majority voting rule. The proposed approach achieves superior classification accuracy compared to recently proposed approaches based on deep learning. © 2019 ieee computer Society. All rights reserved.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Maximally compact and separated features with regular polytope networks 32

Maximally compact and separated features with regular polyto...

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Pernici, Federico Bruni, Matteo Baecchi, Claudio Del Bimbo, Alberto MICC – Media Integration and Communication Center University of Florence Italy

ISBN: (纸本)9781728125060

Convolutional Neural Networks (CNNs) trained with the Softmax loss are widely used classification models for several vision tasks. Typically, a learnable transformation (i.e. the classifier) is placed at the end of such models returning class scores that are further normalized into probabilities by Softmax. This learnable transformation has a fundamental role in determining the network internal feature representation. In this work we show how to extract from CNNs features with the properties of maximum inter-class separability and maximum intra-class compactness by setting the parameters of the classifier transformation as not trainable (i.e. fixed). We obtain features similar to what can be obtained with the well-known "Center Loss" [1] and other similar approaches but with several practical advantages including maximal exploitation of the available feature space representation, reduction in the number of network parameters, no need to use other auxiliary losses besides the Softmax. Our approach unifies and generalizes into a common approach two apparently different classes of methods regarding: discriminative features, pioneered by the Center Loss [1] and fixed classifiers, firstly evaluated in [2]. Preliminary qualitative experimental results provide some insight on the potentialities of our combined strategy. © 2019 ieee computer Society. All rights reserved.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Bidirectional deep residual learning for haze removal 32

Bidirectional deep residual learning for haze removal

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Kim, Guisik Park, Jinhee Ha, Suhyeon Kwon, Junseok School of Computer Science and Engineering Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9781728125060

Recently, low-level vision problems has been addressed using residual learning that can learn a discrepancy between hazy and haze-free images. Following this approach, in this paper, we present a new dehazing method based on the proposed bidirectional residual learning. Our method is implemented by generative adversarial networks (GANs), consisting of two components, namely, haze-removal and haze-reconstruction passes. The method alternates between removal and reconstruction of hazy regions using the residual to produce more accurate haze-free images. For efficient training, we adopt a feature fusion strategy based on extended tree-structures to include more spatial information and apply spectral normalization techniques to GANs. The effectiveness of our method is empirically demonstrated by quantitative and qualitative experiments, indicating that our method outperforms recent state-of-the-art dehazing algorithms. In particular, our approach can be easily used to solve other low-level vision problems such as deraining. © 2019 ieee computer Society. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Photoplethysmography based stratification of blood pressure using multi information fusion artificial neural network

Photoplethysmography based stratification of blood pressure ...

引用

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

作者： Dingliang Wang Xuezhi Yang Xuenan Liu Shuai Fang Likun Ma Longwei Li School of Computer and Information Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China The First Affiliated Hospital University of Science and Technology of China Hefei China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Regular monitoring of blood pressure (BP) is an effective way to prevent cardiovascular diseases, especially for elderly people. At present, BP measurement mainly relies on cuff-based devices which are inconvenient for users and may cause discomfort. Therefore, many new approaches have been proposed to achieve cuff-less BP detection in recent years. However, the accuracy of the existing approaches still needs to be improved. In this study, holistic-based PPG and its first and second derivative features are extracted and a new multi information fusion artificial neural network (MIF-ANN) is designed to effectively fuse and exploit multiple input data. Experimental results on a public database which contains 12000 subjects show that the proposed network can model the relation between Photo-plethysmography (PPG) and BP well, achieving averagely accuracy of 91.33% for 5-category BP stratification. Additionally, this study verified that multi information fusion based on meticulously designed network plays an important role in improving the accuracy of BP detection.

关键词： Feature extraction Neural networks Blood pressure Databases Hypertension Biomedical monitoring Wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Multi-task mutual learning for vehicle re-identification 32

Multi-task mutual learning for vehicle re-identification

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Rajamanoharan, Georgia Kanacı, Aytaç Li, Minxian Gong, Shaogang Vision Semantics Ltd Queen Mary University of London

ISBN: (纸本)9781728125060

Vehicle re-identification (Re-ID) aims to search a specific vehicle instance across non-overlapping camera views. The main challenge of vehicle Re-ID is that the visual appearance of vehicles may drastically changes according to diverse viewpoints and illumination. Most existing vehicle Re-ID models cannot make full use of various complementary vehicle information, e.g. vehicle type and orientation. In this paper, we propose a novel Multi-Task Mutual Learning (MTML) deep model to learn discriminative features simultaneously from multiple branches. Specifically, we design a consensus learning loss function by fusing features from the final convolutional feature maps from all branches. Extensive comparative evaluations demonstrate the effectiveness of our proposed MTML method in comparison to the state-of-the-art vehicle Re-ID techniques on a large-scale benchmark dataset, VeRi-776. We also yield competitive performance on the NVIDIA 2019 AI City Challenge Track 2. © 2019 ieee computer Society. All rights reserved.

关键词： Vehicles

来源：评论

学校读者我要写书评

暂无评论

Improving socially-aware multi-channel human emotion prediction for robot navigation 32

Improving socially-aware multi-channel human emotion predict...

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Bera, Aniket Randhavane, Tanmay Manocha, Dinesh Department of Computer Science University of North Carolina Chapel Hill United States Department of Computer Science University of Maryland College Park United States

ISBN: (纸本)9781728125060

We present a real-time algorithm for emotion-aware navigation of a robot among pedestrians. Our approach estimates time-varying emotional behaviors of pedestrians from their faces and trajectories using a combination of Bayesian-inference, CNN-based learning, and the PAD (Pleasure-Arousal-Dominance) model from psychology. These PAD characteristics are used for long-term path prediction and generating proxemic constraints for each pedestrian. We use a multi-channel model to classify pedestrian characteristics into four emotion categories (happy, sad, angry, neutral). In our validation results, we observe an emotion detection accuracy of 85.33%. We formulate emotion-based proxemic constraints to perform socially-aware robot navigation in low- to medium-density environments. We demonstrate the benefits of our algorithm in simulated environments with tens of pedestrians as well as in a real-world setting with Pepper, a social humanoid robot. © 2019 ieee computer Society. All rights reserved.

关键词： Anthropomorphic robots

来源：评论

学校读者我要写书评

暂无评论

Mesh Variational Autoencoders with Edge Contraction Pooling

Mesh Variational Autoencoders with Edge Contraction Pooling

引用

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

作者： Yu-Jie Yuan Yu-Kun Lai Jie Yang Qi Duan Hongbo Fu Lin Gao Institute of Computing Technology CAS Beijing Key Laboratory of Mobile Computing and Pervasive Device School of Computer Science and Informatics Cardiff University UK SenseTime Research City University of Hong Kong Shenzhen Research Institute of Big Data Shenzhen

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

3D shape analysis is an important research topic in computer vision and graphics. While existing methods have generalized image-based deep learning to meshes using graph-based convolutions, the lack of an effective pooling operation restricts the learning capability of their networks. In this paper, we propose a novel pooling operation for mesh datasets with the same connectivity but different geometry, by building a mesh hierarchy using mesh simplification. For this purpose, we develop a modified mesh simplification method to avoid generating highly irregularly sized triangles. Our pooling operation effectively encodes the correspondence between coarser and finer meshes in the hierarchy. We then present a variational auto-encoder (VAE) structure with the edge contraction pooling and graph-based convolutions, to explore probability latent spaces of 3D surfaces and perform 3D shape generation. Our network requires far fewer parameters than the original mesh VAE and thus can handle denser models thanks to our new pooling operation and convolutional kernels. Our evaluation also shows that our method has better generalization ability and is more reliable in various applications, including shape generation and shape interpolation.

关键词： Three-dimensional displays Shape Strain Interpolation Neural networks Machine learning

来源：评论

学校读者我要写书评

暂无评论

Analyzing U-Net Robustness for Single Cell Nucleus Segmentation from Phase Contrast Images

Analyzing U-Net Robustness for Single Cell Nucleus Segmentat...

引用

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

作者： Chenyi Ling Michael Halter Anne Plant Michael Majurski Jeffrey Stinson Joe Chalfoun Software and Systems Division Information Technology Lab NIST Gaithersburg MD Biosystems and Biomaterials Division Material Measurement Lab NIST Gaithersburg MD

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We quantify the robustness of the semantic segmentation model U-Net, applied to single cell nuclei detection, with respect to the following factors: (1) automated vs manual training annotations, (2) quantity of training data, and (3) microscope image focus. The difficulty of obtaining sufficient volumes of accurate manually annotated training data to create an accurate Convolutional Neural Networks (CNN) model is overcome by the temporary use of fluorescent labels to automate the creation of training datasets using traditional image processing algorithms. The accuracy measurement is computed with respect to manually annotated masks which were also created to evaluate the effectiveness of using automated training set generation via the fluorescent images. The metric to compute the accuracy is the false positive/negative rate of cell nuclei detection. The goal is to maximize the true positive rate while minimizing the false positive rate. We found that automated segmentation of fluorescently labeled nuclei provides viable training data without the need for manual segmentation. A training dataset size of four large stitched images with medium cell density was enough to reach a true positive rate above 88% and a false positive rate below 20%.

关键词： Image segmentation Training computer architecture Microprocessors Microscopy Manuals

来源：评论

学校读者我要写书评

暂无评论

Fast Hardware-Aware Neural Architecture Search

Fast Hardware-Aware Neural Architecture Search

引用

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

作者： Li Lyna Zhang Yuqing Yang Yuhang Jiang Wenwu Zhu Yunxin Liu Microsoft Research Tsinghua University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Designing accurate and efficient convolutional neural architectures for vast amount of hardware is challenging because hardware designs are complex and diverse. This paper addresses the hardware diversity challenge in Neural Architecture Search (NAS). Unlike previous approaches that apply search algorithms on a small, human-designed search space without considering hardware diversity, we propose HURRICANE that explores the automatic hardware-aware search over a much larger search space and a two-stage search algorithm, to efficiently generate tailored models for different types of hardware. Extensive experiments on ImageNet demonstrate that our algorithm outperforms state-of-the-art hardware-aware NAS methods under the same latency constraint on three types of hardware. Moreover, the discovered architectures achieve much lower latency and higher accuracy than current state-of-the-art efficient models. Remarkably, HURRICANE achieves a 76.67% top-1 accuracy on ImageNet with a inference latency of only 16.5 ms for DSP, which is a 3.47% higher accuracy and a 6.35× inference speedup than FBNet-iPhoneX, respectively. For VPU, we achieve a 0.53% higher top-1 accuracy than Proxyless-mobile with a 1.49× speedup. Even for well-studied mobile CPU, we achieve a 1.63% higher top-1 accuracy than FBNet-iPhoneX with a comparable inference latency. HURRICANE also reduces the training time by 30.4% compared to SPOS.

关键词： Hardware computer architecture Graphics processing units Hurricanes Training Measurement

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392页 << < 363 364 365 366 367 368 369 370 371 372 > >>

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：