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

320 篇 会议

馆藏范围

320 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

318 篇 工学
- 318 篇 计算机科学与技术...
- 199 篇 控制科学与工程
- 78 篇 机械工程
- 3 篇 软件工程
- 2 篇 光学工程
- 1 篇 生物工程
200 篇 理学
- 199 篇 系统科学
- 2 篇 物理学
- 1 篇 生物学

主题

135 篇 task analysis
122 篇 computer vision
122 篇 training
90 篇 computer archite...
89 篇 feature extracti...
77 篇 visualization
55 篇 three-dimensiona...
55 篇 computational mo...
45 篇 neural networks
41 篇 semantics
35 篇 cameras
29 篇 image segmentati...
29 篇 convolution
27 篇 face
25 篇 kernel
25 篇 estimation
23 篇 pattern recognit...
23 篇 shape
23 篇 two dimensional ...
22 篇 face recognition

机构

16 篇 univ chinese aca...
10 篇 carnegie mellon ...
8 篇 stanford univ st...
6 篇 cas ctr excellen...
6 篇 univ sci & techn...
6 篇 adobe res san jo...
6 篇 sensetime res pe...
5 篇 univ oxford oxfo...
5 篇 swiss fed inst t...
5 篇 shanghai jiao to...
5 篇 univ calif berke...
5 篇 nvidia santa cla...
5 篇 nanyang technol ...
4 篇 google mountain ...
4 篇 univ edinburgh e...
4 篇 chinese univ hon...
4 篇 microsoft res as...
4 篇 univ tokyo
4 篇 casia cripac peo...
4 篇 univ adelaide ad...

作者

5 篇 kautz jan
4 篇 van gool luc
4 篇 gupta abhinav
4 篇 grauman kristen
4 篇 huang kaiqi
4 篇 belongie serge
4 篇 wang xiaogang
4 篇 hospedales timot...
4 篇 yang ming-hsuan
4 篇 xiang tao
4 篇 wang gang
3 篇 fua pascal
3 篇 girshick ross
3 篇 liu yang
3 篇 darrell trevor
3 篇 feng jiashi
3 篇 borji ali
3 篇 shao jing
3 篇 davis larry s.
3 篇 li fei-fei

语言

320 篇 英文

检索条件"任意字段=31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018"

共 320 条记录，以下是61-70 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：

Making Convolutional Networks Recurrent for Visual Sequence Learning 31

Making Convolutional Networks Recurrent for Visual Sequence ...

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yang, Xiaodong Molchanov, Pavlo Kautz, Jan NVIDIA Santa Clara CA 95051 USA

ISBN: (纸本)9781538664209

Recurrent neural networks (RNNs) have emerged as a powerful model for a broad range of machine learning problems that involve sequential data. While an abundance of work exists to understand and improve RNNs in the context of language and audio signals such as language modeling and speech recognition, relatively little attention has been paid to analyze or modify RNNs for visual sequences, which by nature have distinct properties. In this paper, we aim to bridge this gap and present the first large-scale exploration of RNNs for visual sequence learning. In particular, with the intention of leveraging the strong generalization capacity of pre-trained convolutional neural networks (CNNs), we propose a novel and effective approach, Pre-RNN, to make pre-trained CNNs recurrent by transforming convolutional layers or fully connected layers into recurrent layers. We conduct extensive evaluations on three representative visual sequence learning tasks: sequential face alignment, dynamic hand gesture recognition, and action recognition. Our experiments reveal that PreRNN consistently outperforms the traditional RNNs and achieves stateof- the-art results on the three applications, suggesting that PreRNN is more suitable for visual sequence learning.

关键词： Visualization Logic gates Recurrent neural networks Task analysis Convolution Speech recognition Face recognition

来源：评论

学校读者我要写书评

暂无评论

3D Human Sensing, Action and Emotion recognition in Robot Assisted Therapy of Children with Autism 31

3D Human Sensing, Action and Emotion Recognition in Robot As...

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Marinoiu, Elisabeta Zanfir, Mihai Olaru, Vlad Sminchisescu, Cristian Lund Univ Fac Engn Dept Math Lund Sweden Romanian Acad Inst Math Bucharest Romania

ISBN: (纸本)9781538664209

We introduce new, fine-grained action and emotion recognition tasks defined on non-staged videos, recorded during robot-assisted therapy sessions of children with autism. The tasks present several challenges: a large dataset with long videos, a large number of highly variable actions, children that are only partially visible, have different ages and may show unpredictable behaviour, as well as non-standard camera viewpoints. We investigate how state-of-the-art 3d human pose reconstruction methods perform on the newly introduced tasks and propose extensions to adapt them to deal with these challenges. We also analyze multiple approaches in action and emotion recognition from 3d human pose data, establish several baselines, and discuss results and their implications in the context of child-robot interaction.

关键词： Medical treatment Autism Three-dimensional displays Videos Cameras Robot vision systems

来源：评论

学校读者我要写书评

暂无评论

2D/3D Pose Estimation and Action recognition using Multitask Deep Learning 31

2D/3D Pose Estimation and Action Recognition using Multitask...

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Luvizon, Diogo C. Picard, David Tabia, Hedi Paris Seine Univ CNRS ENSEA ETIS UMR 8051 F-95000 Cergy France Sorbonne Univ CNRS Lab Informat Paris 6 F-75005 Paris France

ISBN: (纸本)9781538664209

Action recognition and human pose estimation are closely related but both problems are generally handled as distinct tasks in the literature. In this work, we propose a multitask framework for jointly 2D and 3D pose estimation from still images and human action recognition from video sequences. We show that a single architecture can be used to solve the two problems in an efficient way and still achieves state-of-the-art results. Additionally, we demonstrate that optimization from end-toend leads to significantly higher accuracy than separated learning. The proposed architecture can be trained with data from different categories simultaneously in a seamlessly way. The reported results on four datasets (MPII, Human3.6M, Penn Action and NTU) demonstrate the effectiveness of our method on the targeted tasks.

关键词： Pose estimation Three-dimensional displays Two dimensional displays Visualization Heating systems Task analysis Skeleton

来源：评论

学校读者我要写书评

暂无评论

Pointwise Convolutional Neural Networks 31

Pointwise Convolutional Neural Networks

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Binh-Son Hua Minh-Khoi Tran Yeung, Sai-Kit Univ Tokyo Tokyo Japan Singapore Univ Technol & Design Singapore Singapore

ISBN: (纸本)9781538664209

Deep learning with 3D data such as reconstructed point clouds and CAD models has received great research interests recently. However, the capability of using point clouds with convolutional neural network has been so far not fully explored. In this paper, we present a convolutional neural network for semantic segmentation and object recognition with 3D point clouds. At the core of our network is pointwise convolution, a new convolution operator that can be applied at each point of a point cloud. Our fully convolutional network design, while being surprisingly simple to implement, can yield competitive accuracy in both semantic segmentation and object recognition task.

关键词： Three-dimensional displays Convolution Semantics Kernel Shape Object recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

Learning a Discriminative Filter Bank within a CNN for Fine-grained recognition 31

Learning a Discriminative Filter Bank within a CNN for Fine-...

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wang, Yaming Morariu, Vlad I. Davis, Larry S. Univ Maryland College Pk MD 20742 USA Adobe Res San Jose CA USA

ISBN: (纸本)9781538664209

Compared to earlier multistage frameworks using CNN features, recent end-to-end deep approaches for finegrained recognition essentially enhance the mid-level learning capability of CNNs. Previous approaches achieve this by introducing an auxiliary network to infuse localization information into the main classification network, or a sophisticated feature encoding method to capture higher order feature statistics. We show that mid-level representation learning can be enhanced within the CNN framework, by learning a bank of convolutional filters that capture class-specific discriminative patches without extra part or bounding box annotations. Such a filter bank is well structured, properly initialized and discriminatively learned through a novel asymmetric multi-stream architecture with convolutional filter supervision and a non-random layer initialization. Experimental results show that our approach achieves state-of-the-art on three publicly available fine-grained recognition datasets (CUB-200-2011, stanford Cars and FGVC-Aircraft). Ablation studies and visualizations are provided to understand our approach.

关键词： Detectors Encoding Convolutional codes Neurons Feature extraction Network architecture Convolution

来源：评论

学校读者我要写书评

暂无评论

Learning a Discriminative Feature Network for Semantic Segmentation 31

Learning a Discriminative Feature Network for Semantic Segme...

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yu, Changqian Wang, Jingbo Peng, Chao Gao, Changxin Yu, Gang Sang, Nong Huazhong Univ Sci & Technol Sch Automat Minist Educ Image Proc & Intelligent Control Key Lab Wuhan Hubei Peoples R China Peking Univ Key Lab Machine Percept Beijing Peoples R China Megvii Inc Face Beijing Peoples R China

ISBN: (纸本)9781538664209

Most existing methods of semantic segmentation still suffer from two aspects of challenges: intra-class inconsistency and inter-class indistinction. To tackle these two problems, we propose a Discriminative Feature Network (DFN), which contains two sub-networks: Smooth Network and Border Network. Specifically, to handle the intra-class inconsistency problem, we specially design a Smooth Network with Channel Attention Block and global average pooling to select the more discriminative features. Furthermore, we propose a Border Network to make the bilateral features of boundary distinguishable with deep semantic boundary supervision. Based on our proposed DFN, we achieve state-of-the-art performance 86.2% mean IOU on PASCAL VOC 2012 and 80.3% mean IOU on Cityscapes dataset.

关键词： Semantics Task analysis Feature extraction Convolution computer architecture computer vision Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Feature Selective Networks for Object Detection 31

Feature Selective Networks for Object Detection

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhai, Yao Fu, Jingjing Lu, Yan Li, Houqiang Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781538664209

Objects for detection usually have distinct characteristics in different sub-regions and different aspect ratios. However, in prevalent two-stage object detection methods, Region-of-Interest (RoI) features are extracted by RoI pooling with little emphasis on these translation-variant feature components. We present feature selective networks to reform the feature representations of Rols by exploiting their disparities among sub-regions and aspect ratios. Our network produces the sub-region attention bank and aspect ratio attention bank for the whole image. The RoI-based sub-region attention map and aspect ratio attention map are selectively pooled from the banks, and then used to refine the original RoI features for RoI classification. Equipped with a lightweight detection subnetwork, our network gets a consistent boost in detection performance based on general ConvNet backbones (ResNet-101, GoogLeNet and VGG-16). Without bells and whistles, our detectors equipped with ResNet-101 achieve more than 3% mAP improvement compared to counterparts on PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO datasets.

关键词： Feature extraction Proposals Object detection Detectors Dimensionality reduction Visualization pattern recognition

来源：评论

学校读者我要写书评

暂无评论

SoS-RSC: A Sum-of-Squares Polynomial Approach to Robustifying Subspace Clustering Algorithms 31

SoS-RSC: A Sum-of-Squares Polynomial Approach to Robustifyin...

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Sznaier, Mario Camps, Octavia Northeastern Univ Elect & Comp Engn Boston MA 02115 USA

ISBN: (纸本)9781538664209

This paper addresses the problem of subspace clustering in the presence of outliers. Typically, this scenario is handled through a regularized optimization, whose computational complexity scales polynomially with the size of the data. Further, the regularization terms need to be manually tuned to achieve optimal performance. To circumvent these difficulties, in this paper we propose an outlier removal algorithm based on evaluating a suitable sum-of squares polynomial, computed directly from the data. This algorithm only requires performing two singular value decompositions of fixed size, and provides certificates on the probability of misclassifying outliers as inliers.

关键词： Optimization Manganese Noise measurement Level set Reliability computer vision Motion segmentation

来源：评论

学校读者我要写书评

暂无评论

xUnit: Learning a Spatial Activation Function for Efficient Image Restoration 31

xUnit: Learning a Spatial Activation Function for Efficient ...

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kligvasser, Idan Shaham, Tamar Rott Michaeli, Tomer Technion Israel Inst Technol Haifa Israel

ISBN: (纸本)9781538664209

In recent years, deep neural networks (DNNs) achieved unprecedented performance in many low-level vision tasks. However, state-of-the-art results are typically achieved by very deep networks, which can reach tens of layers with tens of millions of parameters. To make DNNs implementable on platforms with limited resources, it is necessary to weaken the tradeoff between performance and efficiency. In this paper, we propose a new activation unit, which is particularly suitable for image restoration problems. In contrast to the widespread per-pixel activation units, like ReLUs and sigmoids, our unit implements a learnable nonlinear function with spatial connections. This enables the net to capture much more complex features, thus requiring a significantly smaller number of layers in order to reach the same performance. We illustrate the effectiveness of our units through experiments with state-of-the-art nets for denoising, de-raining, and super resolution, which are already considered to be very small. With our approach, we are able to further reduce these models by nearly 50% without incurring any degradation in performance.

关键词： Noise reduction Task analysis Image restoration computer architecture Image resolution Training Convolution

来源：评论

学校读者我要写书评

暂无评论

CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation 31

CBMV: A Coalesced Bidirectional Matching Volume for Disparit...

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Batsos, Konstantinos Cai, Changjiang Mordohai, Philippos Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (纸本)9781538664209

Recently, there has been a paradigm shift in stereo matching with learning-based methods achieving the best results on all popular benchmarks. The success of these methods is due to the availability of training data with ground truth;training learning-based systems on these datasets has allowed them to surpass the accuracy of conventional approaches based on heuristics and assumptions. Many of these assumptions, however, had been validated extensively and hold for the majority of possible inputs. In this paper, we generate a matching volume leveraging both data with ground truth and conventional wisdom. We accomplish this by coalescing diverse evidence from a bidirectional matching process via random forest classifiers. We show that the resulting matching volume estimation method achieves similar accuracy to purely data-driven alternatives on benchmarks and that it generalizes to unseen data much better. In fact, the results we submitted to the KITTI and ETH3D benchmarks were generated using a classifier trained on the Middlebury 2014 dataset.

关键词： Optimization Training Pipelines computer architecture Estimation Forestry Benchmark testing

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32页 << < 3 4 5 6 7 8 9 10 11 12 > >>

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：