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

6,421 篇 会议
25 篇 期刊文献
3 册 图书

馆藏范围

6,448 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

3,849 篇 工学
- 3,647 篇 计算机科学与技术...
- 1,431 篇 软件工程
- 790 篇 光学工程
- 302 篇 信息与通信工程
- 242 篇 控制科学与工程
- 219 篇 电气工程
- 201 篇 机械工程
- 80 篇 生物医学工程（可授...
- 68 篇 生物工程
- 67 篇 电子科学与技术（可...
- 64 篇 仪器科学与技术
- 36 篇 建筑学
- 33 篇 力学（可授工学、理...
- 33 篇 土木工程
- 33 篇 航空宇航科学与技...
- 26 篇 安全科学与工程
- 22 篇 交通运输工程
- 20 篇 材料科学与工程（可...
- 18 篇 化学工程与技术
1,453 篇 理学
- 945 篇 物理学
- 890 篇 数学
- 352 篇 统计学（可授理学、...
- 134 篇 生物学
- 38 篇 系统科学
- 23 篇 化学
160 篇 管理学
- 110 篇 图书情报与档案管...
- 52 篇 管理科学与工程(可...
- 25 篇 工商管理
112 篇 医学
- 112 篇 临床医学
17 篇 法学
- 17 篇 社会学
12 篇 农学
8 篇 教育学
7 篇 艺术学
6 篇 经济学
2 篇 军事学

主题

2,288 篇 computer vision
789 篇 pattern recognit...
637 篇 cameras
629 篇 computer science
568 篇 face recognition
555 篇 layout
510 篇 image segmentati...
509 篇 conferences
498 篇 shape
445 篇 robustness
439 篇 object recogniti...
388 篇 humans
332 篇 feature extracti...
321 篇 training
303 篇 object detection
262 篇 image recognitio...
257 篇 application soft...
246 篇 lighting
238 篇 image reconstruc...
237 篇 computational mo...

机构

41 篇 microsoft resear...
26 篇 department of co...
21 篇 swiss fed inst t...
21 篇 school of comput...
20 篇 department of co...
19 篇 swiss fed inst t...
19 篇 carnegie mellon ...
18 篇 department of co...
17 篇 department of in...
17 篇 the robotics ins...
17 篇 institute of com...
16 篇 univ sci & techn...
16 篇 robotics institu...
15 篇 tsinghua univ pe...
14 篇 department of el...
14 篇 school of comput...
14 篇 school of comput...
13 篇 univ maryland co...
13 篇 microsoft resear...
13 篇 microsoft resear...

作者

39 篇 timofte radu
28 篇 s.k. nayar
24 篇 huang thomas s.
23 篇 xiaoou tang
22 篇 t. kanade
20 篇 t.s. huang
19 篇 van gool luc
19 篇 t. darrell
19 篇 chellappa rama
18 篇 nayar shree k.
17 篇 a.k. jain
17 篇 a. zisserman
17 篇 jain anil k.
16 篇 g. healey
16 篇 torralba antonio
16 篇 heung-yeung shum
16 篇 zisserman andrew
16 篇 l. van gool
15 篇 m. shah
15 篇 ji qiang

语言

6,445 篇 英文
4 篇 其他

检索条件"任意字段=1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1992"

共 6449 条记录，以下是1581-1590 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：

Learning deep representation for imbalanced classification

Learning deep representation for imbalanced classification

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Huang, Chen Li, Yining Loy, Chen Change Tang, Xiaoou Department of Information Engineering Chinese University of Hong Kong Hong Kong SenseTime Group Limited China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781467388511

Data in vision domain often exhibit highly-skewed class distribution, i.e., most data belong to a few majority classes, while the minority classes only contain a scarce amount of instances. To mitigate this issue, contemporary classification methods based on deep convolutional neural network (CNN) typically follow classic strategies such as class re-sampling or cost-sensitive training. In this paper, we conduct extensive and systematic experiments to validate the effectiveness of these classic schemes for representation learning on class-imbalanced data. We further demonstrate that more discriminative deep representation can be learned by enforcing a deep network to maintain both intercluster and inter-class margins. This tighter constraint effectively reduces the class imbalance inherent in the local data neighborhood. We show that the margins can be easily deployed in standard deep learning framework through quintuplet instance sampling and the associated triple-header hinge loss. The representation learned by our approach, when combined with a simple k-nearest neighbor (kNN) algorithm, shows significant improvements over existing methods on both high- and low-level vision classification tasks that exhibit imbalanced class distribution.

关键词： pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Large scale hard sample mining with Monte Carlo Tree Search

Large scale hard sample mining with Monte Carlo Tree Search

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Canévet, Olivier Fleuret, François Idiap Research Institut Switzerland Switzerland

ISBN: (纸本)9781467388511

We investigate an efficient strategy to collect false positives from very large training sets in the context of object detection. Our approach scales up the standard bootstrapping procedure by using a hierarchical decomposition of an image collection which reflects the statistical regularity of the detector's responses. Based on that decomposition, our procedure uses a Monte Carlo Tree Search to prioritize the sampling toward sub-families of images which have been observed to be rich in false positives, while maintaining a fraction of the sampling toward unexplored sub-families of images. The resulting procedure increases substantially the proportion of false positive samples among the visited ones compared to a naive uniform sampling. We apply experimentally this new procedure to face detection with a collection of ∼100,000 background images and to pedestrian detection with ∼32,000 images. We show that for two standard detectors, the proposed strategy cuts the number of images to visit by half to obtain the same amount of false positives and the same final performance.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Deep decision network for multi-class image classification

Deep decision network for multi-class image classification

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Murthy, Venkatesh N. Singh, Vivek Chen, Terrence Manmatha, R. Comaniciu, Dorin School of Computer Science University of Massachusetts AmherstMA United States Medical Imaging Technologies Siemens Healthcare PrincetonNJ United States

ISBN: (纸本)9781467388511

In this paper, we present a novel Deep Decision Network (DDN) that provides an alternative approach towards building an efficient deep learning network. During the learning phase, starting from the root network node, DDN automatically builds a network that splits the data into disjoint clusters of classes which would be handled by the subsequent expert networks. This results in a tree-like structured network driven by the data. The proposed method provides an insight into the data by identifying the group of classes that are hard to classify and require more attention when compared to others. DDN also has the ability to make early decisions thus making it suitable for timesensitive applications. We validate DDN on two publicly available benchmark datasets: CIFAR-10 and CIFAR-100 and it yields state-of-the-art classification performance on both the datasets. The proposed algorithm has no limitations to be applied to any generic classification problems.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Geometry-informed material recognition

Geometry-informed material recognition

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： DeGol, Joseph Golparvar-Fard, Mani Hoiem, Derek University of Illinois Urbana-Champaign United States

ISBN: (纸本)9781467388511

Our goal is to recognize material categories using images and geometry information. In many applications, such as construction management, coarse geometry information is available. We investigate how 3D geometry (surface normals, camera intrinsic and extrinsic parameters) can be used with 2D features (texture and color) to improve material classification. We introduce a new dataset, GeoMat, which is the first to provide both image and geometry data in the form of: (i) training and testing patches that were extracted at different scales and perspectives from real world examples of each material category, and (ii) a large scale construction site scene that includes 160 images and over 800,000 hand labeled 3D points. Our results show that using 2D and 3D features both jointly and independently to model materials improves classification accuracy across multiple scales and viewing directions for both material patches and images of a large scale construction site scene.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Monocular 3D object detection for autonomous driving

Monocular 3D object detection for autonomous driving

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Chen, Xiaozhi Kundu, Kaustav Zhang, Ziyu Ma, Huimin Fidler, Sanja Urtasun, Raquel Department of Electronic Engineering Tsinghua University China Department of Computer Science University of Toronto Canada

ISBN: (纸本)9781467388511

The goal of this paper is to perform 3D object detection from a single monocular image in the domain of autonomous driving. Our method first aims to generate a set of candidate class-specific object proposals, which are then run through a standard CNN pipeline to obtain highquality object detections. The focus of this paper is on proposal generation. In particular, we propose an energy minimization approach that places object candidates in 3D using the fact that objects should be on the ground-plane. We then score each candidate box projected to the image plane via several intuitive potentials encoding semantic segmentation, contextual information, size and location priors and typical object shape. Our experimental evaluation demonstrates that our object proposal generation approach significantly outperforms all monocular approaches, and achieves the best detection performance on the challenging KITTI benchmark, among published monocular competitors.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Image question answering using convolutional neural network with dynamic parameter prediction

Image question answering using convolutional neural network ...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Noh, Hyeonwoo Seo, Paul Hongsuck Han, Bohyung Department of Computer Science and Engineering POSTECH Korea Republic of

ISBN: (纸本)9781467388511

We tackle image question answering (ImageQA) problem by learning a convolutional neural network (CNN) with a dynamic parameter layer whose weights are determined adaptively based on questions. For the adaptive parameter prediction, we employ a separate parameter prediction network, which consists of gated recurrent unit (GRU) taking a question as its input and a fully-connected layer generating a set of candidate weights as its output. However, it is challenging to construct a parameter prediction network for a large number of parameters in the fully-connected dynamic parameter layer of the CNN. We reduce the complexity of this problem by incorporating a hashing technique, where the candidate weights given by the parameter prediction network are selected using a predefined hash function to determine individual weights in the dynamic parameter layer. The proposed network-joint network with the CNN for ImageQA and the parameter prediction network-is trained end-to-end through back-propagation, where its weights are initialized using a pre-trained CNN and GRU. The proposed algorithm illustrates the state-of-the-art performance on all available public ImageQA benchmarks.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Object Co-segmentation via graph optimized-flexible manifold ranking

Object Co-segmentation via graph optimized-flexible manifold...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Quan, Rong Han, Junwei Zhang, Dingwen Nie, Feiping School of Automation Center for OPTIMAL Northwestern Polytechnical University Xi'an710072 China School of Computer Science Center for OPTIMAL Northwestern Polytechnical University Xi'an710072 China

ISBN: (纸本)9781467388511

Aiming at automatically discovering the common objects contained in a set of relevant images and segmenting them as foreground simultaneously, object co-segmentation has become an active research topic in recent years. Although a number of approaches have been proposed to address this problem, many of them are designed with the misleading assumption, unscalable prior, or low flexibility and thus still suffer from certain limitations, which reduces their capability in the real-world scenarios. To alleviate these limitations, we propose a novel two-stage co-segmentation framework, which introduces the weak background prior to establish a globally close-loop graph to represent the common object and union background separately. Then a novel graph optimized-flexible manifold ranking algorithm is proposed to flexibly optimize the graph connection and node labels to co-segment the common objects. Experiments on three image datasets demonstrate that our method outperforms other state-of-the-art methods.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Harnessing object and scene semantics for large-scale video understanding

Harnessing object and scene semantics for large-scale video ...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Wu, Zuxuan Fu, Yanwei Jiang, Yu-Gang Sigal, Leonid Shanghai Key Lab of Intel. Info. Processing School of Computer Science Fudan University China Disney Research United States

ISBN: (纸本)9781467388511

Large-scale action recognition and video categorization are important problems in computer vision. To address these problems, we propose a novel object- and scene-based semantic fusion network and representation. Our semantic fusion network combines three streams of information using a three-layer neural network: (i) frame-based low-level CNN features, (ii) object features from a state-of-the-art large-scale CNN object-detector trained to recognize 20K classes, and (iii) scene features from a state-of-the-art CNN scene-detector trained to recognize 205 scenes. The trained network achieves improvements in supervised activity and video categorization in two complex large-scale datasets - ActivityNet and FCVID, respectively. Further, by examining and back propagating information through the fusion network, semantic relationships (correlations) between video classes and objects/scenes can be discovered. These video class-object/video class-scene relationships can in turn be used as semantic representation for the video classes themselves. We illustrate effectiveness of this semantic representation through experiments on zero-shot action/video classification and clustering.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Deep exemplar 2D-3D detection by adapting from real to rendered views

Deep exemplar 2D-3D detection by adapting from real to rende...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Massa, Francisco Russell, Bryan C. Aubry, Mathieu ENPC Marne-la-ValleF-77455 France Adobe Research United States UC Berkeley United States

ISBN: (纸本)9781467388511

This paper presents an end-to-end convolutional neural network (CNN) for 2D-3D exemplar detection. We demonstrate that the ability to adapt the features of natural images to better align with those of CAD rendered views is critical to the success of our technique. We show that the adaptation can be learned by compositing rendered views of textured object models on natural images. Our approach can be naturally incorporated into a CNN detection pipeline and extends the accuracy and speed benefits from recent advances in deep learning to 2D-3D exemplar detection. We applied our method to two tasks: instance detection, where we evaluated on the IKEA dataset [36], and object category detection, where we out-perform Aubry et al. [3] for "chair" detection on a subset of the Pascal VOC dataset.

关键词： Rendering (computer graphics)

来源：评论

学校读者我要写书评

暂无评论

Visual7W: Grounded question answering in images

Visual7W: Grounded question answering in images

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Zhu, Yuke Groth, Oliver Bernstein, Michael Fei-Fei, Li Computer Science Department Stanford University United States Computer Science Department Dresden University of Technology Germany

ISBN: (纸本)9781467388511

We have seen great progress in basic perceptual tasks such as object recognition and detection. However, AI models still fail to match humans in high-level vision tasks due to the lack of capacities for deeper reasoning. Recently the new task of visual question answering (QA) has been proposed to evaluate a model's capacity for deep image understanding. Previous works have established a loose, global association between QA sentences and images. However, many questions and answers, in practice, relate to local regions in the images. We establish a semantic link between textual descriptions and image regions by object-level grounding. It enables a new type of QA with visual answers, in addition to textual answers used in previous work. We study the visual QA tasks in a grounded setting with a large collection of 7W multiple-choice QA pairs. Furthermore, we evaluate human performance and several baseline models on the QA tasks. Finally, we propose a novel LSTM model with spatial attention to tackle the 7W QA tasks.

关键词： Long short-term memory

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 << < 155 156 157 158 159 160 161 162 163 164 > >>

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：