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

1,925 篇 会议
233 册 图书
24 篇 期刊文献

馆藏范围

2,181 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

1,604 篇 工学
- 1,333 篇 计算机科学与技术...
- 465 篇 软件工程
- 251 篇 电气工程
- 236 篇 机械工程
- 186 篇 光学工程
- 179 篇 信息与通信工程
- 119 篇 控制科学与工程
- 95 篇 生物工程
- 62 篇 生物医学工程（可授...
- 41 篇 仪器科学与技术
- 39 篇 电子科学与技术（可...
- 30 篇 化学工程与技术
- 21 篇 安全科学与工程
- 19 篇 材料科学与工程（可...
- 15 篇 交通运输工程
- 13 篇 建筑学
444 篇 理学
- 258 篇 物理学
- 203 篇 数学
- 109 篇 生物学
- 57 篇 统计学（可授理学、...
- 22 篇 化学
228 篇 医学
- 200 篇 临床医学
- 26 篇 基础医学(可授医学...
- 22 篇 特种医学
136 篇 管理学
- 83 篇 图书情报与档案管...
- 59 篇 管理科学与工程(可...
- 19 篇 工商管理
27 篇 艺术学
- 27 篇 设计学（可授艺术学...
16 篇 农学
- 15 篇 作物学
15 篇 法学
- 13 篇 社会学
9 篇 教育学
7 篇 经济学
5 篇 文学
5 篇 军事学

主题

321 篇 computer vision
286 篇 pattern recognit...
166 篇 artificial intel...
119 篇 feature extracti...
114 篇 computer imaging...
99 篇 image processing...
82 篇 face recognition
67 篇 training
60 篇 object detection
60 篇 image segmentati...
54 篇 computer applica...
53 篇 deep learning
51 篇 robustness
47 篇 computer graphic...
46 篇 cameras
45 篇 visualization
43 篇 semantics
38 篇 object recogniti...
37 篇 shape
36 篇 information syst...

机构

89 篇 univ chinese aca...
67 篇 chinese acad sci...
59 篇 national laborat...
56 篇 chinese acad sci...
50 篇 univ chinese aca...
36 篇 chinese univ hon...
36 篇 university of ch...
31 篇 institute of aut...
27 篇 chinese acad sci...
25 篇 school of artifi...
23 篇 univ sci & techn...
22 篇 chinese academy ...
18 篇 chinese acad sci...
17 篇 chinese univ hon...
16 篇 chinese acad sci...
16 篇 univ chinese aca...
15 篇 national laborat...
15 篇 computer vision ...
14 篇 tsinghua univers...
14 篇 department of in...

作者

32 篇 wang xiaogang
29 篇 lu hanqing
28 篇 tan tieniu
28 篇 wang jinqiao
23 篇 li stan z.
22 篇 pal umapada
21 篇 huang kaiqi
21 篇 lei zhen
21 篇 qiao yu
19 篇 tieniu tan
19 篇 hu weiming
18 篇 tang xiaoou
17 篇 xilin chen
15 篇 wang liang
15 篇 chen xilin
15 篇 cheng jian
14 篇 liu jing
14 篇 tang ming
13 篇 xiaoou tang
13 篇 shiguang shan

语言

2,160 篇 英文
19 篇 中文
8 篇 其他
1 篇 土耳其文

检索条件"任意字段=7th Chinese Conference on Pattern Recognition and Computer Vision"

共 2182 条记录，以下是501-510 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：

Depth Supporting Semantic Segmentation via Deep Neural Markov Random Field 7th

Depth Supporting Semantic Segmentation via Deep Neural Marko...

引用

7th chinese conference on pattern recognition (CCPR)

作者： Su, Wen Wang, Zengfu Chinese Acad Sci Hefei Inst Phys Sci Inst Intelligent Machines Hefei Anhui Peoples R China Univ Sci & Technol China Hefei Anhui Peoples R China Natl Engn Lab Speech & Language Informat Proc Hefei Anhui Peoples R China

ISBN: (纸本)9789811030024;9789811030017

Semantic segmentation is of great importance to various vision applications. Depth information plays an important role in human visual system to help people obtain meaningful segmentation results, but it is not well considered by most existing segmentation methods. In this paper, we address the problem of semantic segmentation by incorporating depth information via deep neural Markov Random Field. In our method, the color image and its corresponding depth map are first fed to a convolutional neural network. then, a deconvolution approach is performed on the network output to obtain the pixelwise prediction in terms of the probability of labels assigned to pixels. Finally, the dense prediction is used to design unary term and pairwise term, which are determined by pixels coordinate, color and depth. Experiments are conducted on several public datasets to illustrate the effectiveness of the proposed method. On the PASCAL VOC 2011 test dataset, experimental results show that our method can get accurate results when compared with the ground truth. On the PASCAL VOC 2012 dataset and NYUDv2 dataset, the proposed method can obtain competitive results.

关键词： Semantic segmentation Depth CNNs MRF

来源：评论

学校读者我要写书评

暂无评论

Backscatter Compensated Photometric Stereo with 3 Sources 27

Backscatter Compensated Photometric Stereo with 3 Sources

引用

27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Tsiotsios, Chourmouzios Angelopoulou, Maria E. Kim, Tae-Kyun Davison, Andrew J. Univ London Imperial Coll Sci Technol & Med London SW7 2AZ England

ISBN: (纸本)9781479951178

Photometric stereo offers the possibility of object shape reconstruction via reasoning about the amount of light reflected from oriented surfaces. However, in murky media such as sea water, the illuminating light interacts with the medium and some of it is backscattered towards the camera. Due to this additive light component, the standard Photometric Stereo equations lead to poor quality shape estimation. Previous authors have attempted to reformulate the approach but have either neglected backscatter entirely or disregarded its non-uniformity on the sensor when camera and lights are close to each other. We show that by compensating effectively for the backscatter component, a linear formulation of Photometric Stereo is allowed which recovers an accurate normal map using only 3 lights. Our backscatter compensation method for point-sources can be used for estimating the uneven backscatter directly from single images without any prior knowledge about the characteristics of the medium or the scene. We compare our method with previous approaches through extensive experimental results, where a variety of objects are imaged in a big water tank whose turbidity is systematically increased, and show reconstruction quality which degrades little relative to clean water results even with a very significant scattering level.

关键词： backscatter photometric stereo restoration scattering underwater vision

来源：评论

学校读者我要写书评

暂无评论

Jensen-Shannon boosting learning for object recognition

Jensen-Shannon boosting learning for object recognition

引用

2005 IEEE computer Society conference on computer vision and pattern recognition, CVPR 2005

作者： Huang, Xiangsheng Li, Stan Z. Wang, Yangsheng CASIA-SAIT HCI Joint Lab. Institute of Automation Chinese Academy of Science Beijing 100080 China Institute of Automation Chinese Academy of Science Beijing 100080 China

ISBN: (纸本)0769523722

In this paper, we propose a novel learning method, called Jensen-Shannon Boosting (JSBoost) and demonstrate its application to object recognition. JSBoost incorporates Jensen-Shannon (JS) divergence [2] into AdaBoost learning. JS divergence is advantageous in that it provides more appropriate measure of dissimilarity between two classes and it is numerically more stable than other measures such as Kullback-Leibler (KL) divergence (see [2]). the best features are iteratively learned by maximizing the projected JS divergence, based on which best weak classifiers are derived. the weak classifiers are combined into a strong one by minimizing the recognition error. JSBoost learning is demonstrated with face object recognition using a local binary pattern (LBP) [13] based representation. JSBoost selects the best LBP features from thousands of candidate features and constructs a strong classifier based on the selected features. JSBoost empirically produces better face recognition results than other AdaBoost variants such as RealBoost [12], GentleBoost [5] and KL-Boost [7], as demonstrated by experiments.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Residual Attention Network for Image Classification 30

Residual Attention Network for Image Classification

引用

30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Fei Jiang, Mengqing Qian, Chen Yang, Shuo Li, Cheng Zhang, Honggang Wang, Xiaogang Tang, Xiaoou SenseTime Grp Ltd Hong Kong Hong Kong Peoples R China Tsinghua Univ Beijing Peoples R China Chinese Univ Hong Kong Hong Kong Hong Kong Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781538604571

In this work, we propose "Residual Attention Network", a convolutional neural network using attention mechanism which can incorporate with state-of-art feed forward network architecture in an end-to-end training fashion. Our Residual Attention Network is built by stacking Attention Modules which generate attention-aware features. the attention-aware features from different modules change adaptively as layers going deeper. Inside each Attention Module, bottom-up top-down feedforward structure is used to unfold the feedforward and feedback attention process into a single feedforward process. Importantly, we propose attention residual learning to train very deep Residual Attention Networks which can be easily scaled up to hundreds of layers. Extensive analyses are conducted on CIFAR-10 and CIFAR-100 datasets to verify the effectiveness of every module mentioned above. Our Residual Attention Network achieves state-of-the-art object recognition performance on three benchmark datasets including CIFAR-10 (3.90% error), CIFAR-100 (20.45% error) and ImageNet (4.8% single model and single crop, top-5 error). Note that, our method achieves 0.6% top-1 accuracy improvement with 46% trunk depth and 69% forward FLOPs comparing to ResNet-200. the experiment also demonstrates that our network is robust against noisy labels.

关键词： Neural networks Training Stacking Logic gates Noise measurement Image color analysis image colour analysis Noise measurement Logic gates Stacking Neural network FEEDFORWARD Image classification Functional training RESIDUAL

来源：评论

学校读者我要写书评

暂无评论

Hardware-Efficient Guided Image Filtering For Multi-Label Problem 30

Hardware-Efficient Guided Image Filtering For Multi-Label Pr...

引用

30th IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Dai, Longquan Yuan, Mengke Li, Zechao Zhang, Xiaopeng Tang, Jinhui Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China

ISBN: (纸本)9781538604571

the Guided Filter (GF) is well-known for its linear complexity. However, when filtering an image with an n-channel guidance, GF needs to invert an nxn matrix for each pixel. To the best of our knowledge existing matrix inverse algorithms are inefficient on current hardwares. this shortcoming limits applications of multichannel guidance in computation intensive system such as multi-label system. We need a new GF-like filter that can perform fast multichannel image guided filtering. Since the optimal linear complexity of GF cannot be minimized further, the only way thus is to bring all potentialities of current parallel computing hardwares into full play. In this paper we propose a hardware-efficient Guided Filter (HGF), which solves the efficiency problem of multichannel guided image filtering and yields competent results when applying it to multi-label problems with synthesized polynomial multichannel guidance. Specifically, in order to boost the filtering performance, HGF takes a new matrix inverse algorithm which only involves two hardware-efficient operations: element-wise arithmetic calculations and box filtering. In order to break the linear model restriction, HGF synthesizes a polynomial multichannel guidance to introduce nonlinearity. Benefiting from our polynomial guidance and hardware-efficient matrix inverse algorithm, HGF not only is more sensitive to the underlying structure of guidance but also achieves the fastest computing speed. Due to these merits, HGF obtains state-of-the-art results in terms of accuracy and efficiency in the computation intensive multi-label systems.

关键词： image filtering Guidance Interleukin-6 SOS1 gene HGF gene Hepatocyte Growth Factor Filtration Inverse matrix Filters arithmetic Economic Resources linear complexity

来源：评论

学校读者我要写书评

暂无评论

Multimodal Topic and Sentiment recognition for chinese Data Based on Pre-trained Encoders 6th

Multimodal Topic and Sentiment Recognition for Chinese Data ...

引用

6th chinese conference on pattern recognition and computer vision (PRCV)

作者： Chen, Qian Chen, Siting Wu, Changli Peng, Jun Xiamen Univ Sch Informat Xiamen 361005 Fujian Peoples R China

ISBN: (纸本)9789819985395;9789819985401

With the rapid development of mobile internet technology, massive amounts of multimodal data have emerged from major online platforms. However, defining multimodal topic categories is a more subjective task, and the lack of a public chinese dataset hinders the progress of the task. And fine-grained multimodal sentiment classification is extremely challenging, especially for aspect-level classification. In this paper, we build a chinese dataset based on Weibo for multimodal topic classification and propose a pre-trained encoder-based model for topic classification and fine-grained sentiment classification. the experimental results on Multi-ZOL and our proposed dataset show that the proposed model outperforms the benchmark model on the multi-modal topic classification task, achieving an accuracy of 0.99. On the fine-grained multimodal sentiment classification task, the proposed model achieves an accuracy of 0.5860 and a macro-F1 of 0.5791, which is competitive with SOTA.

关键词： multimodal topic classification sentiment classification

来源：评论

学校读者我要写书评

暂无评论

Incorporating Spiking Neural Network for Dynamic vision Emotion Analysis 6th

Incorporating Spiking Neural Network for Dynamic Vision Emot...

引用

6th chinese conference on pattern recognition and computer vision (PRCV)

作者： Wang, Binqiang Liang, Xiaoqiang Shandong Mass Informat Technol Res Inst Jinan 250101 Peoples R China Inspur Beijing Elect Informat Ind Co Ltd Beijing Peoples R China China North Vehicle Res Inst Beijing 100072 Peoples R China

ISBN: (纸本)9789819985579;9789819985586

In the domain of affective computing, researchers have sought to enhance the performance of models and algorithms by leveraging the complementarity of multimodal information. However, the rapid emergence of new modalities has outpaced the development of suitable datasets, posing a challenge in keeping up with the advancements in modal sensing technology. the collection and analysis of multimodal data present intricate and substantial tasks. To address the partial missing data challenge within the research community, we have curated a novel homogeneous multimodal gesture emotion recognition dataset, augmenting existing datasets through meticulous analysis. this dataset not only fills the gaps in homogeneous multimodal data but also opens up new avenues for emotion recognition research. Additionally, we propose a pseudo dual-flow network based on this dataset, establishing its potential application in the affective computing community. Experimental findings indicate the feasibility of utilizing traditional visual information and spiking visual information derived from homogeneous multimodal data for visual emotion recognition.

关键词： Spiking neural network Affective Computing Dynamic vision sensor

来源：评论

学校读者我要写书评

暂无评论

Text-Aware Single Image Specular Highlight Removal 4th

Text-Aware Single Image Specular Highlight Removal

引用

4th chinese conference on pattern recognition and computer vision (PRCV)

作者： Hou, Shiyu Wang, Chaoqun Quan, Weize Jiang, Jingen Yan, Dong-Ming Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit NLPR Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9783030880132;9783030880125

Removing undesirable specular highlight from a single input image is of crucial importance to many computer vision and graphics tasks. Existing methods typically remove specular highlight for medical images and specific-object images, however, they cannot handle the images with text. In addition, the impact of specular highlight on text recognition is rarely studied by text detection and recognition community. therefore, in this paper, we first raise and study the text-aware single image specular highlight removal problem. the core goal is to improve the accuracy of text detection and recognition by removing the highlight from text images. To tackle this challenging problem, we first collect three high-quality datasets with fine-grained annotations, which will be appropriately released to facilitate the relevant research. then, we design a novel two-stage network, which contains a highlight detection network and a highlight removal network. the output of highlight detection network provides additional information about highlight regions to guide the subsequent highlight removal network. Moreover, we suggest a measurement set including the end-to-end text detection and recognition evaluation and auxiliary visual quality evaluation. Extensive experiments on our collected datasets demonstrate the superior performance of the proposed method.

关键词： Specular highlight removal Text-awareness Datasets Neural network

来源：评论

学校读者我要写书评

暂无评论

Lip temporal pattern analysis for automatic visual speech recognition

Lip temporal pattern analysis for automatic visual speech re...

引用

7th International conference on Signal Processing

作者： Xie, L Cai, XL Fu, ZH Jiang, DM Zhao, RC Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China

ISBN: (纸本)7121002159

this paper presents a novel approach to processing temporal lip motion information for dynamic visual feature extraction in visual speech recognition. the long-time Lip TenipoRA1 patterns (LipTRAPs) of visual phonemes are introduced to analyze the nature of lip shape changes when uttering speech. A dynamic visual feature is also proposed based on the LipTRAPs. Visual speech recognition experiments on a connected-digits task show that the LipTRAP feature can yield significant WRR improvments than conventional delta features.

关键词： visual speech recognition lipreading feature extraction lip temporal pattern

来源：评论

学校读者我要写书评

暂无评论

P2SGrad: Refined Gradients for Optimizing Deep Face Models 32

P2SGrad: Refined Gradients for Optimizing Deep Face Models

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Xiao Zhao, Rui Yan, Junjie Gao, Mengya Qiao, Yu Wang, Xiaogang Li, Hongsheng Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol SIAT SenseTime Joint Lab Shenzhen Peoples R China

ISBN: (纸本)9781728132938

Cosine-based softmax losses [20, 29, 27, 3] significantly improve the performance of deep face recognition networks. However, these losses always include sensitive hyper-parameters which can make training process unstable, and it is very tricky to set suitable hyper parameters for a specific dataset. this paper addresses this challenge by directly designing the gradients for training in an adaptive manner. We first investigate and unify previous cosine softmax losses from the perspective of gradients. this unified view inspires us to propose a novel gradient called P2SGrad (Probability-to-Similarity Gradient), which leverages a cosine similarity instead of classification probability to control the gradients for updating neural network parameters. P2SGrad is adaptive and hyper-parameter free, which makes training process more efficient and faster. We evaluate our P2SGrad on three face recognition benchmarks, LFW [7], MegaFace [8], and IJB-C [16]. the results show that P2SGrad is stable in training, robust to noise, and achieves state-of-the-art performance on all the three benchmarks.

关键词： And Body Pose Biometrics Categorization Deep Learning Face Gesture Optimization Methods recognition: Detection Retrieval

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219页 << < 47 48 49 50 51 52 53 54 55 56 > >>

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：