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

858 篇 会议

馆藏范围

858 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

853 篇 工学
- 838 篇 计算机科学与技术...
- 27 篇 软件工程
- 15 篇 生物工程
- 10 篇 光学工程
- 9 篇 机械工程
- 8 篇 信息与通信工程
- 5 篇 控制科学与工程
- 3 篇 电气工程
- 3 篇 电子科学与技术（可...
- 3 篇 生物医学工程（可授...
- 2 篇 化学工程与技术
- 1 篇 建筑学
- 1 篇 交通运输工程
31 篇 理学
- 15 篇 生物学
- 10 篇 数学
- 9 篇 物理学
- 5 篇 统计学（可授理学、...
- 3 篇 化学
- 1 篇 地质学
5 篇 管理学
- 3 篇 图书情报与档案管...
- 2 篇 管理科学与工程(可...
3 篇 医学
- 3 篇 临床医学
- 2 篇 基础医学(可授医学...
- 1 篇 公共卫生与预防医...
- 1 篇 药学(可授医学、理...

主题

362 篇 deep learning
359 篇 recognition: det...
358 篇 categorization
357 篇 retrieval
128 篇 vision applicati...
121 篇 low-level vision
119 篇 representation l...
92 篇 vision + languag...
69 篇 segmentation
68 篇 image and video ...
63 篇 scene analysis a...
60 篇 grouping and sha...
54 篇 face
52 篇 gesture
51 篇 and body pose
51 篇 vision + graphic...
48 篇 computational ph...
45 篇 video analytics
44 篇 action recogniti...
42 篇 datasets and eva...

机构

27 篇 peng cheng lab p...
26 篇 univ chinese aca...
23 篇 chinese univ hon...
17 篇 sensetime res pe...
17 篇 nanyang technol ...
15 篇 univ sci & techn...
15 篇 carnegie mellon ...
15 篇 tsinghua univ pe...
15 篇 sun yat sen univ...
14 篇 johns hopkins un...
12 篇 stanford univ st...
11 篇 peking univ peop...
11 篇 incept inst arti...
11 篇 huawei noahs ark...
10 篇 facebook ai res ...
10 篇 univ maryland co...
10 篇 shanghai jiao to...
10 篇 univ calif berke...
9 篇 georgia inst tec...
9 篇 swiss fed inst t...

作者

10 篇 zhang lei
10 篇 wang xiaogang
10 篇 liu wei
10 篇 shao ling
9 篇 zheng wei-shi
9 篇 loy chen change
8 篇 timofte radu
7 篇 chun se young
7 篇 tao dacheng
7 篇 lin liang
7 篇 lin dahua
7 篇 zuo wangmeng
7 篇 yan junjie
7 篇 cheng ming-ming
6 篇 van gool luc
6 篇 chen xilin
6 篇 tian qi
6 篇 rohrbach marcus
6 篇 ji rongrong
6 篇 khan fahad shahb...

语言

858 篇 英文

检索条件"任意字段=32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019"

共 858 条记录，以下是111-120 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：

TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translation 32

TransGaGa: Geometry-Aware Unsupervised Image-to-Image Transl...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wu, Wayne Cao, Kaidi Li, Cheng Qian, Chen Loy, Chen Change SenseTime Res Hong Kong Peoples R China Stanford Univ Stanford CA 94305 USA Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781728132938

Unsupervisedimage-to-image translationaims at learning a mapping between two visual domains. However learning a translationacross large geometry variationsalways ends up with failure. In this work, we present a novel disentangle-and-translateframework to tackle the complex objects image-to-image translationtask. Instead of learning the mapping on the image space directly, we disentangle image space into a Cartesianproductof the appearance and the geometry latent spaces. Specifically, we first introduce a geometry prior loss and a conditional VAE loss to encourage the network to learn independent but complementary representations. The translation is then built on appearanceand geometry space separately. Extensive experiments demonstrate the superiorperformance of our method to other state-of-the-art approaches, especially in the challenging near-rigidand non-rigidobjects translation tasks. In addition, by taking different exemplars as the appearance references, our method also supports multimodal translation. Project page: https://wywu. github. io/projects/TGaGa/***

关键词： And Body Pose Deep Learning Face Gesture Image and Video Synthesis Representation Learning vision Applications and Syst

来源：评论

学校读者我要写书评

暂无评论

Latent Space Autoregression for Novelty Detection 32

Latent Space Autoregression for Novelty Detection

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Abati, Davide Porrello, Angelo Calderara, Simone Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9781728132938

Novelty detection is commonly referred to as the discrimination of observations that do not conform to a learned model of regularity. Despite its importance in different application settings, designing a novelty detector is utterly complex due to the unpredictable nature of novelties and its inaccessibility during the training procedure, factors which expose the unsupervised nature of the problem. In our proposal, we design a general framework where we equip a deep autoencoder with a parametric density estimator that learns the probability distribution underlying its latent representations through an autoregressive procedure. We show that a maximum likelihood objective, optimized in conjunction with the reconstruction of normal samples, effectively acts as a regularizer for the task at hand, by minimizing the differential entropy of the distribution spanned by latent vectors. In addition to providing a very general formulation, extensive experiments of our model on publicly available datasets deliver on-par or superior performances if compared to state-of-the-art methods in one-class and video anomaly detection settings. Differently from prior works, our proposal does not make any assumption about the nature of the novelties, making our work readily applicable to diverse contexts.

关键词： Deep Learning Representation Learning Scene Analysis and Understanding Video Analytics vision Applications and

来源：评论

学校读者我要写书评

暂无评论

IM-Net for High Resolution Video Frame Interpolation 32

IM-Net for High Resolution Video Frame Interpolation

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Peleg, Tomer Szekely, Pablo Sabo, Doron Sendik, Omry Samsung Israel R&D Ctr Ramat Gan Israel

ISBN: (纸本)9781728132938

Video frame interpolation is a long-studied problem in the video processing field. Recently, deep learning approaches have been applied to this problem, showing impressive results on low-resolution benchmarks. However, these methods do not scale-up favorably to high resolutions. Specifically, when the motion exceeds a typical number of pixels, their interpolation quality is degraded. Moreover, their run time renders them impractical for real-time applications. In this paper we propose IM-Net: an interpolated motion neural network. We use an economic structured architecture and end-to-end training with multi-scale tailored losses. In particular, we formulate interpolated motion estimation as classification rather than regression. IM-Net outperforms previous methods by more than 1.3dB (PSNR) on a high resolution version of the recently introduced Vimeo triplet dataset. Moreover, the network runs in less than 33msec on a single GPU for HD resolution.

关键词： Datasets and Evaluation Deep Learning Image and Video Synthesis Low-level vision Motion and Tracking vision Applications

来源：评论

学校读者我要写书评

暂无评论

Not All Frames Are Equal: Weakly-Supervised Video Grounding with Contextual Similarity and Visual Clustering Losses 32

Not All Frames Are Equal: Weakly-Supervised Video Grounding ...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Shi, Jing Xu, Jia Gong, Boqing Xu, Chenliang Univ Rochester Rochester NY 14627 USA Tencent AI Lab Bellevue WA USA

ISBN: (纸本)9781728132938

We investigate the problem of weakly-supervised video grounding, where only video-level sentences are provided. This is a challenging task, and previous Multi-Instance Learning (MIL) based image grounding methods turn to fail in the video domain. Recent work attempts to decompose the video-level MIL into frame-level MIL by applying weighted sentence-frame ranking loss over frames, but it is not robust and does not exploit the rich temporal information in videos. In this work, we address these issues by extending frame-level MIL with a false positive frame-bag constraint and modeling the visual feature consistency in the video. In specific, we design a contextual similarity between semantic and visual features to deal with sparse objects association across frames. Furthermore, we leverage temporal coherence by strengthening the clustering effect of similar features in the visual space. We conduct an extensive evaluation on YouCookII and RoboWatch datasets, and demonstrate our method significantly outperforms prior state-of-the-art methods.

关键词： Video Analytics vision + Language

来源：评论

学校读者我要写书评

暂无评论

Locating Objects Without Bounding Boxes 32

Locating Objects Without Bounding Boxes

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Ribera, Javier Guera, David Chen, Yuhao Delp, Edward J. Purdue Univ Video & Image Proc Lab VIPER W Lafayette IN 47907 USA

ISBN: (纸本)9781728132938

Recent advances in convolutional neural networks (CNN) have achieved remarkable results in locating objects in images. In these networks, the training procedure usually requires providing bounding boxes or the maximum number of expected objects. In this paper, we address the task of estimating object locations without annotated bounding boxes which are typically hand-drawn and time consuming to label. We propose a loss function that can be used in any fully convolutional network (FCN) to estimate object locations. This loss function is a modification of the average Hausdorff distance between two unordered sets of points. The proposed method has no notion of bounding boxes, region proposals, or sliding windows. We evaluate our method with three datasets designed to locate people's heads, pupil centers and plant centers. We outperform state-of-the-art generic object detectors and methods fine-tuned for pupil tracking.

关键词： Categorization recognition: Detection Retrieval

来源：评论

学校读者我要写书评

暂无评论

Dynamic Scene Deblurring with Parameter Selective Sharing and Nested Skip Connections 32

Dynamic Scene Deblurring with Parameter Selective Sharing an...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Gao, Hongyun Tao, Xin Shen, Xiaoyong Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Tencent Youtu Lab Shenzhen Peoples R China

ISBN: (纸本)9781728132938

Dynamic Scene deblurring is a challenging low-level vision task where spatially variant blur is caused by many factors, e.g., camera shake and object motion. Recent study has made significant progress. Compared with the parameter independence scheme [19] and parameter sharing scheme [33], we develop the general principle for con straining the deblurring network structure by proposing the generic and effective selective sharing scheme. Inside the subnetwork of each scale, we propose a nested skip connection structure for the nonlinear transformation modules to replace stacked convolution layers or residual blocks. Besides, we build a new large dataset of blurred/sharp image pairs towards better restoration quality. Comprehensive experimental results show that our parameter selective sharing scheme, nested skip connection structure, and the new dataset are all significant to set a new state-of-the-art in dynamic scene deblurring.

关键词： Deep Learning Low-level vision

来源：评论

学校读者我要写书评

暂无评论

Multi-adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection 32

Multi-adversarial Discriminative Deep Domain Generalization ...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Shao, Rui Lan, Xiangyuan Li, Jiawei Yuen, Pong C. Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781728132938

Face presentation attacks have become an increasingly critical issue in the face recognition community. Many face anti-spoofing methods have been proposed, but they cannot generalize well on "unseen" attacks. This work focuses on improving the generalization ability of face anti spoofing methods from the perspective of the domain generalization. We propose to learn a generalized feature space via a novel multi-adversarial discriminative deep domain generalization framework. In this framework, a multi adversarial deep domain generalization is performed under a dual-force triplet-mining constraint. This ensures that the learned feature space is discriminative and shared by multiple source domains, and thus is more generalized to new face presentation attacks. An auxiliary face depth supervision is incorporated to further enhance the generalization ability. Extensive experiments on four public datasets validate the effectiveness of the proposed method.

关键词： Biometrics vision Applications and Systems

来源：评论

学校读者我要写书评

暂无评论

What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment 32

What and How Well You Performed? A Multitask Learning Approa...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Parmar, Paritosh Morris, Brendan Tran Univ Nevada Las Vegas NV 89154 USA

ISBN: (纸本)9781728132938

Can performance on the task of action quality assessment (AQA) be improved by exploiting a description of the action and its quality? Current AQA and skills assessment approaches propose to learn features that serve only one task - estimating the final score. In this paper, we propose to learn spatio-temporal features that explain three related tasks - fine-grained action recognition, commentary generation, and estimating the AQA score. A new multitask-AQA dataset, the largest to date, comprising of 1412 diving samples was collected to evaluate our approach (http://***/datasets. html). We show that our MTL approach outperforms STL approach using two different kinds of architectures: C3D-AVG and MSCADC. The C3D-AVG-MTL approach achieves the new state-of-the-art performance with a rank correlation of 90.44%. Detailed experiments were performed to show that MTL offers better generalization than STL, and representations from action recognition models are not sufficient for the AQA task and instead should be learned.

关键词： Action recognition And Body Pose Datasets and Evaluation Face Gesture Video Analytics

来源：评论

学校读者我要写书评

暂无评论

Contrastive Adaptation Network for Unsupervised Domain Adaptation 32

Contrastive Adaptation Network for Unsupervised Domain Adapt...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kang, Guoliang Jiang, Lu Yang, Yi Hauptmann, Alexander G. Univ Technol Sydney CAI Ultimo Australia Google AI Mountain View CA USA Baidu Res Beijing Peoples R China Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781728132938

Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while manual annotations are only available in the source domain. Previous methods minimize the domain discrepancy neglecting the class information, which may lead to misalignment and poor generalization performance. To address this issue, this paper proposes Contrastive Adaptation Network (CAN) optimizing a new metric which explicitly models the intra-class domain discrepancy and the inter-class domain discrepancy. We design an alternating update strategy for training CAN in an end-to-end manner. Experiments on two real-world benchmarks Office-31 and VisDA-2017 demonstrate that CAN performs favorably against the state-of-the-art methods and produces more discriminative features.

关键词： Categorization Deep Learning recognition: Detection Representation Learning Retrieval

来源：评论

学校读者我要写书评

暂无评论

SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity through Low-Bit Quantization 32

SeerNet: Predicting Convolutional Neural Network Feature-Map...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Cao, Shijie Ma, Lingxiao Xiao, Wencong Zhang, Chen Liu, Yunxin Zhang, Lintao Nie, Lanshun Yang, Zhi Harbin Inst Technol Harbin Peoples R China Peking Univ Beijing Peoples R China Beihang Univ Beijing Peoples R China Microsoft Res Redmond WA 98052 USA

ISBN: (纸本)9781728132938

In this paper, we present a novel and general method to accelerate convolutional neural network (CNN) inference by taking advantage of feature map sparsity. We experimentally demonstrate that a highly quantized version of the original network is sufficient in predicting the output sparsity accurately, and verify that leveraging such sparsity in inference incurs negligible accuracy drop compared with the original network. To accelerate inference, for each convolution layer, our approach first obtains a binary sparsity mask of the output feature maps by running inference on a quantized version of the original network layer and then conducts a full-precision sparse convolution to find out the precise values of the non-zero outputs. Compared with existing work, our approach avoids the overhead of training additional auxiliary networks, while is still applicable to general CNN networks without being limited to certain application domains.

关键词： Deep Learning vision Applications and Systems

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86页 << < 8 9 10 11 12 13 14 15 16 17 > >>

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：