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

103 篇 会议
77 篇 期刊文献

馆藏范围

180 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

122 篇 工学
- 75 篇 计算机科学与技术...
- 74 篇 软件工程
- 32 篇 信息与通信工程
- 21 篇 生物工程
- 20 篇 光学工程
- 18 篇 机械工程
- 8 篇 控制科学与工程
- 8 篇 化学工程与技术
- 7 篇 生物医学工程（可授...
- 5 篇 仪器科学与技术
- 4 篇 电气工程
- 4 篇 建筑学
- 3 篇 土木工程
- 2 篇 力学（可授工学、理...
- 2 篇 材料科学与工程（可...
- 2 篇 冶金工程
- 2 篇 电子科学与技术（可...
- 2 篇 测绘科学与技术
- 2 篇 交通运输工程
- 2 篇 安全科学与工程
80 篇 理学
- 40 篇 物理学
- 29 篇 数学
- 23 篇 生物学
- 9 篇 统计学（可授理学、...
- 8 篇 化学
31 篇 管理学
- 22 篇 图书情报与档案管...
- 12 篇 管理科学与工程(可...
3 篇 法学
- 3 篇 社会学
2 篇 医学
- 2 篇 基础医学(可授医学...
- 2 篇 临床医学
2 篇 艺术学

主题

12 篇 convolution
12 篇 feature extracti...
10 篇 image segmentati...
10 篇 image edge detec...
10 篇 image reconstruc...
9 篇 semantics
7 篇 face
7 篇 computer vision
6 篇 three-dimensiona...
6 篇 pixels
6 篇 training
5 篇 generative adver...
5 篇 writing
5 篇 face recognition
5 篇 image color anal...
4 篇 distillation
4 篇 vectors
4 篇 optical resolvin...
4 篇 text recognition
4 篇 mathematical mod...

机构

40 篇 university of ch...
40 篇 shenzhen key lab...
31 篇 national key lab...
28 篇 computer vision ...
26 篇 shenzhen key lab...
22 篇 faculty of compu...
21 篇 siat branch shen...
19 篇 shanghai ai labo...
17 篇 sensetime resear...
16 篇 shenzhen key lab...
11 篇 shanghai artific...
10 篇 department of in...
8 篇 shanghai ai lab
7 篇 department of st...
7 篇 the chinese univ...
6 篇 shanghai jiao to...
6 篇 shenzhen key lab...
6 篇 shenzhen key lab...
6 篇 the university o...
6 篇 university of ma...

作者

60 篇 qiao yu
28 篇 yu qiao
27 篇 dong chao
17 篇 wang yali
17 篇 pal umapada
17 篇 lu tong
16 篇 umapada pal
16 篇 tong lu
16 篇 palaiahnakote sh...
15 篇 shivakumara pala...
11 篇 chao dong
10 篇 he junjun
10 篇 chen shifeng
9 篇 chen xiangyu
9 篇 gu jinjin
9 篇 peng xiaojiang
9 篇 liu jianzhuang
8 篇 ren jimmy s.
7 篇 blumenstein mich...
7 篇 zhou zhipeng

语言

176 篇 英文
3 篇 其他
1 篇 中文

检索条件"机构=Shenzhen Key Lab of Computer Vision and Pattern Recognition"

共 180 条记录，以下是11-20 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：

Automatic motion-guided video stylization and personalization 11

Automatic motion-guided video stylization and personalizatio...

引用

19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

作者： Cao, Chen Chen, Shifeng Zhang, Wei Tang, Xiaoou Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450306164

Video stylization transfers a source video into an artistic version while maintaining temporal coherence between adjacent frames. In this paper, we formulate the unsupervised example-based video stylization with Markov random field model. In our algorithm, we implement an improved optical flow algorithm to maintain temporal coherence while improve the accuracy of estimation along motion boundaries. We also extend our algorithm to the application of video personalization, in which human faces keep clear and distinguishable. A series of techniques are fused in video personalization, including face detection and alignment, motion flow, skin detection, and illumination blending. Given a source video and a style template image, our algorithm produces the stylized and/or personalized video(s) automatically. Experimental results demonstrate that our algorithm performs excellently in both video stylization and personalization. Copyright 2011 ACM.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Edge-preserving single image super-resolution 11

Edge-preserving single image super-resolution

引用

19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

作者： Zhou, Qiang Chen, Shifeng Liu, Jianzhuang Tang, Xiaoou Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450306164

This paper proposes a novel approach to single image super-resolution. First, an image up-sampling scheme is proposed which takes the advantages of both bilateral filtering and mean shift image segmentation. Then we use a shock filter to enhance strong edges in the initial up-sampling result and obtain an intermediate high-resolution image. Finally, we enforce a reconstruction constraint on the high-resolution image so that fine details can be inferred by back projection. Since strong edges in the intermediate result are enhanced, ringing artifacts can be suppressed in the back projection step. We compare our algorithm with several state-of-the-art image super-resolution algorithms. Qualitative and quantitative experimental results demonstrate that our approach performs the best. Copyright 2011 ACM.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Visual Compositional Learning for Human-Object Interaction Detection 16th

Visual Compositional Learning for Human-Object Interaction D...

引用

16th European Conference on computer vision, ECCV 2020

作者： Hou, Zhi Peng, Xiaojiang Qiao, Yu Tao, Dacheng UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030585549

Human-Object interaction (HOI) detection aims to localize and infer relationships between human and objects in an image. It is challenging because an enormous number of possible combinations of objects and verbs types forms a long-tail distribution. We devise a deep Visual Compositional Learning (VCL) framework, which is a simple yet efficient framework to effectively address this problem. VCL first decomposes an HOI representation into object and verb specific features, and then composes new interaction samples in the feature space via stitching the decomposed features. The integration of decomposition and composition enables VCL to share object and verb features among different HOI samples and images, and to generate new interaction samples and new types of HOI, and thus largely alleviates the long-tail distribution problem and benefits low-shot or zero-shot HOI detection. Extensive experiments demonstrate that the proposed VCL can effectively improve the generalization of HOI detection on HICO-DET and V-COCO and outperforms the recent state-of-the-art methods on HICO-DET. Code is available at https://***/zhihou7/VCL. © 2020, Springer Nature Switzerland AG.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

RBF-Softmax: Learning Deep Representative Prototypes with Radial Basis Function Softmax 1

引用

16th European Conference on computer vision, ECCV 2020

作者： Zhang, Xiao Zhao, Rui Qiao, Yu Li, Hongsheng CUHK-SenseTime Joint Lab The Chinese University of Hong Kong Hong Kong SenseTime Research Hong Kong ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9783030585747

ISBN: (纸本)9783030585730

Deep neural networks have achieved remarkable successes in learning feature representations for visual classification. However, deep features learned by the softmax cross-entropy loss generally show excessive intra-class variations. We argue that, because the traditional softmax losses aim to optimize only the relative differences between intra-class and inter-class distances (logits), it cannot obtain representative class prototypes (class weights/centers) to regularize intra-class distances, even when the training is converged. Previous efforts mitigate this problem by introducing auxiliary regularization losses. But these modified losses mainly focus on optimizing intra-class compactness, while ignoring keeping reasonable relations between different class prototypes. These lead to weak models and eventually limit their performance. To address this problem, this paper introduces a novel Radial Basis Function (RBF) distances to replace the commonly used inner products in the softmax loss function, such that it can adaptively assign losses to regularize the intra-class and inter-class distances by reshaping the relative differences, and thus creating more representative prototypes of classes to improve optimization. The proposed RBF-Softmax loss function not only effectively reduces intra-class distances, stabilizes the training behavior, and reserves ideal relations between prototypes, but also significantly improves the testing performance. Experiments on visual recognition benchmarks including MNIST, CIFAR-10/100, and ImageNet demonstrate that the proposed RBF-Softmax achieves better results than cross-entropy and other state-of-the-art classification losses. The code is at https://***/2han9x1a0release/RBF-Softmax. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Efficient Image Super-Resolution Using Vast-Receptive-Field Attention 17th

Efficient Image Super-Resolution Using Vast-Receptive-Field ...

引用

17th European Conference on computer vision, ECCV 2022

作者： Zhou, Lin Cai, Haoming Gu, Jinjin Li, Zheyuan Liu, Yingqi Chen, Xiangyu Qiao, Yu Dong, Chao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shanghai AI Laboratory Shanghai China The University of Sydney Sydney Australia University of Macau Zhuhai China

ISBN: (纸本)9783031250620

The attention mechanism plays a pivotal role in designing advanced super-resolution (SR) networks. In this work, we design an efficient SR network by improving the attention mechanism. We start from a simple pixel attention module and gradually modify it to achieve better super-resolution performance with reduced parameters. The specific approaches include: (1) increasing the receptive field of the attention branch, (2) replacing large dense convolution kernels with depthwise separable convolutions, and (3) introducing pixel normalization. These approaches paint a clear evolutionary roadmap for the design of attention mechanisms. Based on these observations, we propose VapSR, the Vast-receptive-field Pixel attention network. Experiments demonstrate the superior performance of VapSR. VapSR outperforms the present lightweight networks with even fewer parameters. And the light version of VapSR can use only 21.68% and 28.18% parameters of IMDB and RFDN to achieve similar performances to those networks. The code and models are available at https://***/zhoumumu/VapSR. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Efficient Image Super-Resolution Using Pixel Attention 16th

Efficient Image Super-Resolution Using Pixel Attention

引用

Workshops held at the 16th European Conference on computer vision, ECCV 2020

作者： Zhao, Hengyuan Kong, Xiangtao He, Jingwen Qiao, Yu Dong, Chao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030670696

This work aims at designing a lightweight convolutional neural network for image super resolution (SR). With simplicity bare in mind, we construct a pretty concise and effective network with a newly proposed pixel attention scheme. Pixel attention (PA) is similar as channel attention and spatial attention in formulation. The difference is that PA produces 3D attention maps instead of a 1D attention vector or a 2D map. This attention scheme introduces fewer additional parameters but generates better SR results. On the basis of PA, we propose two building blocks for the main branch and the reconstruction branch, respectively. The first one—SC-PA block has the same structure as the Self-Calibrated convolution but with our PA layer. This block is much more efficient than conventional residual/dense blocks, for its two-branch architecture and attention scheme. While the second one—U-PA block combines the nearest-neighbor upsampling, convolution and PA layers. It improves the final reconstruction quality with little parameter cost. Our final model—PAN could achieve similar performance as the lightweight networks—SRResNet and CARN, but with only 272K parameters (17.92% of SRResNet and 17.09% of CARN). The effectiveness of each proposed component is also validated by ablation study. The code is available at https://***/zhaohengyuan1/PAN. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Conditional Sequential Modulation for Efficient Global Image Retouching 16th

Conditional Sequential Modulation for Efficient Global Image...

引用

16th European Conference on computer vision, ECCV 2020

作者： He, Jingwen Liu, Yihao Qiao, Yu Dong, Chao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT - SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030586003

Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation. Practically, photo retouching can be accomplished by a series of image processing operations. In this paper, we investigate some commonly-used retouching operations and mathematically find that these pixel-independent operations can be approximated or formulated by multi-layer perceptrons (MLPs). Based on this analysis, we propose an extremely light-weight framework - Conditional Sequential Retouching Network (CSRNet) - for efficient global image retouching. CSRNet consists of a base network and a condition network. The base network acts like an MLP that processes each pixel independently and the condition network extracts the global features of the input image to generate a condition vector. To realize retouching operations, we modulate the intermediate features using Global Feature Modulation (GFM), of which the parameters are transformed by condition vector. Benefiting from the utilization of 1 × 1 convolution, CSRNet only contains less than 37 k trainable parameters, which is orders of magnitude smaller than existing learning-based methods. Extensive experiments show that our method achieves state-of-the-art performance on the benchmark MIT-Adobe FiveK dataset quantitively and qualitatively. Code is available at https://***/hejingwenhejingwen/CSRNet. © 2020, Springer Nature Switzerland AG.

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

Learning to Predict Context-Adaptive Convolution for Semantic Segmentation 16th

Learning to Predict Context-Adaptive Convolution for Semanti...

引用

16th European Conference on computer vision, ECCV 2020

作者： Liu, Jianbo He, Junjun Qiao, Yu Ren, Jimmy S. Li, Hongsheng CUHK-SenseTime Joint Laboratory The Chinese University of Hong Kong Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SenseTime Research Hong Kong

ISBN: (纸本)9783030585945

Long-range contextual information is essential for achieving high-performance semantic segmentation. Previous feature re-weighting methods demonstrate that using global context for re-weighting feature channels can effectively improve the accuracy of semantic segmentation. However, the globally-sharing feature re-weighting vector might not be optimal for regions of different classes in the input image. In this paper, we propose a Context-adaptive Convolution Network (CaC-Net) to predict a spatially-varying feature weighting vector for each spatial location of the semantic feature maps. In CaC-Net, a set of context-adaptive convolution kernels are predicted from the global contextual information in a parameter-efficient manner. When used for convolution with the semantic feature maps, the predicted convolutional kernels can generate the spatially-varying feature weighting factors capturing both global and local contextual information. Comprehensive experimental results show that our CaC-Net achieves superior segmentation performance on three public datasets, PASCAL Context, PASCAL VOC 2012 and ADE20K. © 2020, Springer Nature Switzerland AG.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Facial landmark localization based on hierarchical pose regression with cascaded random ferns 13

Facial landmark localization based on hierarchical pose regr...

引用

21st ACM International Conference on Multimedia, MM 2013

作者： Zhang, Zhanpeng Zhang, Wei Liu, Jianzhuang Tang, Xiaoou Shenzhen Key Lab of Computer Vision and Pattern Recognition Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology China Department of Information Engineering Chinese University of Hong Kong Hong Kong Media Lab Huawei Technologies Co. Ltd. China

ISBN: (纸本)9781450324045

The main challenge of facial landmark localization in realworld application is that the large changes of head pose and facial expressions cause substantial image appearance variations. To avoid high dimensional regression in the 3D and 2D facial pose spaces simultaneously, we propose a hierarchical pose regression approach, estimating the head rotation, facial components and landmarks hierarchically. The regression process works in a unified cascaded fern framework. We present generalized gradient boosted ferns (GBFs) for the regression framework, which give better performance than traditional ferns. The framework also achieves real time performance. We verify our method on the latest benchmark datasets. The results show that it outperforms state-of-theart methods in both accuracy and speed. Copyright © 2013 ACM.

关键词： Regression analysis

来源：评论

学校读者我要写书评

暂无评论

Multi-feature canonical correlation analysis for face photo-sketch image retrieval 13

Multi-feature canonical correlation analysis for face photo-...

引用

21st ACM International Conference on Multimedia, MM 2013

作者： Gong, Dihong Li, Zhifeng Liu, Jianzhuang Qiao, Yu Shenzhen Key Lab of Computer Vision and Pattern Recognition Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology China Media Lab Huawei Technologies Co. Ltd. China Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450324045

Automatic face photo-sketch image retrieval has attracted great attention in recent years due to its important applications in real life. The major difficulty in automatic face photo-sketch image retrieval lies in the fact that there exists great discrepancy between the different image modalities (photo and sketch). In order to reduce such discrepancy and improve the performance of automatic face photo-sketch image retrieval, we propose a new framework called multi-feature canonical correlation analysis (MCCA) to effectively address this problem. The MCCA is an extension and improvement of the canonical correlation analysis (CCA) algorithm using multiple features combined with two different random sampling methods in feature space and sample space. In this framework, we first represent each photo or sketch using a patch-based local feature representation scheme, in which histograms of oriented gradients (HOG) and multi-scale local binary pattern (MLBP) serve as the local descriptors. Canonical correlation analysis (CCA) is then performed on a collection of random subspaces to construct an ensemble of classifiers for photo-sketch image retrieval. Extensive experiments on two public-domain face photo-sketch datasets (CUFS and CUFSF) clearly show that the proposed approach obtains a substantial improvement over the state-of-the-art. Copyright © 2013 ACM.

关键词： Image 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18页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：