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

22,771 篇 会议
112 篇 期刊文献
23 册 图书

馆藏范围

22,905 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,398 篇 工学
- 10,880 篇 计算机科学与技术...
- 3,450 篇 软件工程
- 2,430 篇 机械工程
- 1,721 篇 光学工程
- 1,010 篇 控制科学与工程
- 998 篇 电气工程
- 761 篇 信息与通信工程
- 393 篇 仪器科学与技术
- 337 篇 生物工程
- 257 篇 生物医学工程（可授...
- 215 篇 电子科学与技术（可...
- 113 篇 化学工程与技术
- 112 篇 安全科学与工程
- 98 篇 测绘科学与技术
- 92 篇 交通运输工程
- 86 篇 建筑学
- 82 篇 土木工程
3,362 篇 医学
- 3,348 篇 临床医学
- 79 篇 基础医学(可授医学...
3,250 篇 理学
- 1,953 篇 物理学
- 1,664 篇 数学
- 567 篇 统计学（可授理学、...
- 484 篇 生物学
- 245 篇 系统科学
- 109 篇 化学
506 篇 管理学
- 299 篇 图书情报与档案管...
- 219 篇 管理科学与工程(可...
- 75 篇 工商管理
252 篇 艺术学
- 252 篇 设计学（可授艺术学...
62 篇 法学
- 59 篇 社会学
40 篇 农学
25 篇 教育学
19 篇 经济学
11 篇 军事学
3 篇 文学

主题

10,126 篇 computer vision
4,025 篇 pattern recognit...
2,900 篇 training
1,958 篇 computational mo...
1,792 篇 cameras
1,758 篇 visualization
1,485 篇 shape
1,466 篇 image segmentati...
1,447 篇 feature extracti...
1,412 篇 three-dimensiona...
1,288 篇 robustness
1,169 篇 computer archite...
1,144 篇 layout
1,142 篇 computer science
1,134 篇 semantics
1,071 篇 object detection
1,043 篇 conferences
1,009 篇 benchmark testin...
967 篇 codes
810 篇 face recognition

机构

135 篇 univ sci & techn...
118 篇 univ chinese aca...
118 篇 chinese univ hon...
110 篇 carnegie mellon ...
99 篇 tsinghua univers...
99 篇 microsoft resear...
94 篇 swiss fed inst t...
92 篇 zhejiang univ pe...
82 篇 university of sc...
81 篇 zhejiang univers...
77 篇 shanghai ai lab ...
77 篇 university of ch...
72 篇 shanghai jiao to...
68 篇 microsoft res as...
65 篇 national laborat...
65 篇 alibaba grp peop...
64 篇 tsinghua univ pe...
63 篇 adobe research
60 篇 peking univ peop...
59 篇 peng cheng labor...

作者

78 篇 van gool luc
72 篇 timofte radu
63 篇 zhang lei
45 篇 luc van gool
40 篇 yang yi
37 篇 loy chen change
33 篇 xiaoou tang
33 篇 li stan z.
33 篇 qi tian
32 篇 sun jian
31 篇 liu yang
31 篇 li fei-fei
30 篇 chen chen
30 篇 tian qi
30 篇 pascal fua
29 篇 darrell trevor
28 篇 ying shan
27 篇 li xin
27 篇 vasconcelos nuno
27 篇 hanqing lu

语言

22,844 篇 英文
35 篇 其他
20 篇 中文
5 篇 土耳其文
2 篇 日文

检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22906 条记录，以下是4741-4750 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：

Hybrid Message Passing with Performance-Driven Structures for Facial Action Unit Detection

Hybrid Message Passing with Performance-Driven Structures fo...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Song, Tengfei Cui, Zijun Zheng, Wenming Ji, Qiang Southeast Univ Nanjing Peoples R China Rensselaer Polytech Inst Troy NY 12181 USA

ISBN: (纸本)9781665445092

Message passing neural network has been an effective method to represent dependencies among nodes by propagating messages. However, most of message passing algorithms focus on one structure and messages are estimated by one single approach. For real-world data, like facial action units (AUs), the dependencies may vary in terms of different expressions and individuals. In this paper, we propose a novel hybrid message passing neural network with performance-driven structures (HMP-PS), which combines complementary message passing methods and captures more possible structures in a Bayesian manner. Particularly, a performance-driven Monte Carlo Markov Chain sampling method is proposed for generating high performance graph structures. Besides, hybrid message passing is proposed to combine different types of messages, which provide the complementary information. The contribution of each type of message is adaptively adjusted along with different inputs. The experiments on two widely used benchmark datasets, i.e., BP4D and DISFA, validate that our proposed method can achieve the state-of-the-art performance.

关键词： Gold computer vision Monte Carlo methods Databases Message passing Neural networks Markov processes

来源：评论

学校读者我要写书评

暂无评论

Regularizing Neural Networks via Adversarial Model Perturbation

Regularizing Neural Networks via Adversarial Model Perturbat...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zheng, Yaowei Zhang, Richong Mao, Yongyi Beihang Univ BDBC Beijing Peoples R China Beihang Univ SKLSDE Beijing Peoples R China Univ Ottawa Sch EECS Ottawa ON Canada

ISBN: (纸本)9781665445092

Effective regularization techniques are highly desired in deep learning for alleviating overfitting and improving generalization. This work proposes a new regularization scheme, based on the understanding that the flat local minima of the empirical risk cause the model to generalize better. This scheme is referred to as adversarial model perturbation (AMP), where instead of directly minimizing the empirical risk, an alternative "AMP loss" is minimized via SGD. Specifically, the AMP loss is obtained from the empirical risk by applying the "worst" norm-bounded perturbation on each point in the parameter space. Comparing with most existing regularization schemes, AMP has strong theoretical justifications, in that minimizing the AMP loss can be shown theoretically to favour flat local minima of the empirical risk. Extensive experiments on various modern deep architectures establish AMP as a new state of the art among regularization schemes.

关键词： Deep learning computer vision Codes Perturbation methods Computational modeling Neural networks Tools

来源：评论

学校读者我要写书评

暂无评论

Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time

Semi-Supervised 3D Hand-Object Poses Estimation with Interac...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liu, Shaowei Jiang, Hanwen Xu, Jiarui Liu, Sifei Wang, Xiaolong Univ Calif San Diego La Jolla CA 92093 USA NVIDIA Santa Clara CA USA

ISBN: (纸本)9781665445092

Estimating 3D hand and object pose from a single image is an extremely challenging problem: hands and objects are often self-occluded during interactions, and the 3D annotations are scarce as even humans cannot directly label the ground-truths from a single image perfectly. To tackle these challenges, we propose a unified framework for estimating the 3D hand and object poses with semi-supervised learning. We build a joint learning framework where we perform explicit contextual reasoning between hand and object representations. Going beyond limited 3D annotations in a single image, we leverage the spatial-temporal consistency in large-scale hand-object videos as a constraint for generating pseudo labels in semi-supervised learning. Our method not only improves hand pose estimation in challenging real-world dataset, but also substantially improve the object pose which has fewer ground-truths per instance. By training with large-scale diverse videos, our model also generalizes better across multiple out-of-domain datasets. Project page and code: https://***/Semi-Hand-Object.

关键词： Training computer vision Three-dimensional displays Annotations Filtering Pose estimation Semisupervised learning

来源：评论

学校读者我要写书评

暂无评论

Learnable Motion Coherence for Correspondence Pruning

Learnable Motion Coherence for Correspondence Pruning

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liu, Yuan Liu, Lingjie Lin, Cheng Dong, Zhen Wang, Wenping Univ Hong Kong Hong Kong Peoples R China Saarland Informat Campus MPI Informat Saarbrucken Germany Wuhan Univ Wuhan Peoples R China Texas A&M Univ College Stn TX 77843 USA

ISBN: (纸本)9781665445092

Motion coherence is an important clue for distinguishing true correspondences from false ones. Modeling motion coherence on sparse putative correspondences is challenging due to their sparsity and uneven distributions. Existing works on motion coherence are sensitive to parameter settings and have difficulty in dealing with complex motion patterns. In this paper, we introduce a network called Laplacian Motion Coherence Network (LMCNet) to learn motion coherence property for correspondence pruning. We propose a novel formulation of fitting coherent motions with a smooth function on a graph of correspondences and show that this formulation allows a closed-form solution by graph Laplacian. This closed-form solution enables us to design a differentiable layer in a learning framework to capture global motion coherence from putative correspondences. The global motion coherence is further combined with local coherence extracted by another local layer to robustly detect inlier correspondences. Experiments demonstrate that LMCNet has superior performances to the state of the art in relative camera pose estimation and correspondences pruning of dynamic scenes(1).

关键词： computer vision Laplace equations Closed-form solutions Pose estimation Fitting Dynamics Neural networks

来源：评论

学校读者我要写书评

暂无评论

DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation

DANNet: A One-Stage Domain Adaptation Network for Unsupervis...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wu, Xinyi Wu, Zhenyao Guo, Hao Ju, Lili Wang, Song Univ South Carolina Columbia SC 29208 USA Farsee2 Technol Ltd Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781665445092

Semantic segmentation of nighttime images plays an equally important role as that of daytime images in autonomous driving, but the former is much more challenging due to poor illuminations and arduous human annotations. In this paper, we propose a novel domain adaptation network (DANNet) for nighttime semantic segmentation without using labeled nighttime image data. It employs an adversarial training with a labeled daytime dataset and an unlabeled dataset that contains coarsely aligned day-night image pairs. Specifically, for the unlabeled day-night image pairs, we use the pixel-level predictions of static object categories on a daytime image as a pseudo supervision to segment its counterpart nighttime image. We further design a re-weighting strategy to handle the inaccuracy caused by misalignment between day-night image pairs and wrong predictions of daytime images, as well as boost the prediction accuracy of small objects. The proposed DANNet is the first one-stage adaptation framework for nighttime semantic segmentation, which does not train additional day-night image transfer models as a separate pre-processing stage. Extensive experiments on Dark Zurich and Nighttime Driving datasets show that our method achieves state-of-the-art performance for nighttime semantic segmentation.

关键词： Training Bridges Image segmentation computer vision Annotations Semantics Neural networks

来源：评论

学校读者我要写书评

暂无评论

A Dual Iterative Refinement Method for Non-rigid Shape Matching

A Dual Iterative Refinement Method for Non-rigid Shape Match...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Xiang, Rui Lai, Rongjie Zhao, Hongkai UC Dept Math Irvine CA 92697 USA Rensselaer Polytech Inst Dept Math Troy NY 12181 USA Duke Univ Dept Math Durham NC 27706 USA

ISBN: (纸本)9781665445092

In this work, a robust and efficient dual iterative refinement (DIR) method is proposed for dense correspondence between two nearly isometric shapes. The key idea is to use dual information, such as spatial and spectral, or local and global features, in a complementary and effective way, and extract more accurate information from current iteration to use for the next iteration. In each DIR iteration, starting from current correspondence, a zoom-in process at each point is used to select well matched anchor pairs by a local mapping distortion criterion. These selected anchor pairs are then used to align spectral features (or other appropriate global features) whose dimension adaptively matches the capacity of the selected anchor pairs. Thanks to the effective combination of complementary information in a data-adaptive way, DIR is not only efficient but also robust to render accurate results within a few iterations. By choosing appropriate dual features, DIR has the flexibility to handle patch and partial matching as well. Our comprehensive experiments on various data sets demonstrate the superiority of DIR over other state-of-the-art methods in terms of both accuracy and efficiency.

关键词： computer vision Image analysis Shape Stability criteria Feature extraction Distortion Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression

Bottom-Up Human Pose Estimation Via Disentangled Keypoint Re...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Geng, Zigang Sun, Ke Xiao, Bin Zhang, Zhaoxiang Wang, Jingdong Univ Sci & Technol China Beijing Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Inst Automat Ctr Artificial Intelligence & RobotHKISI Beijing Peoples R China Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9781665445092

In this paper, we are interested in the bottom-up paradigm of estimating human poses from an image. We study the dense keypoint regression framework that is previously inferior to the keypoint detection and grouping framework. Our motivation is that regressing keypoint positions accurately needs to learn representations that focus on the keypoint regions. We present a simple yet effective approach, named disentangled keypoint regression (DEKR). We adopt adaptive convolutions through pixel-wise spatial transformer to activate the pixels in the keypoint regions and accordingly learn representations from them. We use a multi-branch structure for separate regression: each branch learns a representation with dedicated adaptive convolutions and regresses one keypoint. The resulting disentangled representations are able to attend to the keypoint regions, respectively, and thus the keypoint regression is spatially more accurate. We empirically show that the proposed direct regression method outperforms keypoint detection and grouping methods and achieves superior bottom-up pose estimation results on two benchmark datasets, COCO and Crowd-Pose. The code and models are available at https: //***/HRNet/DEKR.

关键词： Convolutional codes Location awareness computer vision Pose estimation Focusing Object detection Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Self-supervised Video Hashing via Bidirectional Transformers

Self-supervised Video Hashing via Bidirectional Transformers

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Shuyan Li, Xiu Lu, Jiwen Zhou, Jie Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China Tsinghua Univ Grad Sch Shenzhen Beijing Peoples R China

ISBN: (纸本)9781665445092

Most existing unsupervised video hashing methods are built on unidirectional models with less reliable training objectives, which underuse the correlations among frames and the similarity structure between videos. To enable efficient scalable video retrieval, we propose a self-supervised video Hashing method based on Bidirectional Transformers (BTH). Based on the encoder-decoder structure of transformers, we design a visual cloze task to fully exploit the bidirectional correlations between frames. To unveil the similarity structure between unlabeled video data, we further develop a similarity reconstruction task by establishing reliable and effective similarity connections in the video space. Furthermore, we develop a cluster assignment task to exploit the structural statistics of the whole dataset such that more discriminative binary codes can be learned. Extensive experiments implemented on three public benchmark datasets, FCVID, ActivityNet and YFCC, demonstrate the superiority of our proposed approach.

关键词： Training Hash functions Visualization computer vision Correlation Benchmark testing Transformers

来源：评论

学校读者我要写书评

暂无评论

Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias

<i>Camera Pose Matters</i>: Improving Depth Prediction by Mi...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Yunhan Kong, Shu Fowlkes, Charless UC Irvine Irvine CA 92697 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665445092

Monocular depth predictors are typically trained on large-scale training sets which are naturally biased w.r.t the distribution of camera poses. As a result, trained predictors fail to make reliable depth predictions for testing examples captured under uncommon camera poses. To address this issue, we propose two novel techniques that exploit the camera pose during training and prediction. First, we introduce a simple perspective-aware data augmentation that synthesizes new training examples with more diverse views by perturbing the existing ones in a geometrically consistent manner. Second, we propose a conditional model that exploits the per-image camera pose as prior knowledge by encoding it as a part of the input. We show that jointly applying the two methods improves depth prediction on images captured under uncommon and even never-before-seen camera poses. We show that our methods improve performance when applied to a range of different predictor architectures. Lastly, we show that explicitly encoding the camera pose distribution improves the generalization performance of a synthetically trained depth predictor when evaluated on real images.

关键词： Training computer vision Image coding Handheld computers Perturbation methods Cameras Encoding

来源：评论

学校读者我要写书评

暂无评论

Tackling the Ill-Posedness of Super-Resolution through Adaptive Target Generation

Tackling the Ill-Posedness of Super-Resolution through Adapt...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Jo, Younghyun Oh, Seoung Wug Vajda, Peter Kim, Seon Joo Yonsei Univ Seoul South Korea Adobe Res San Jose CA USA Facebook Menlo Pk CA USA

ISBN: (纸本)9781665445092

By the one-to-many nature of the super-resolution (SR) problem, a single low-resolution (LR) image can be mapped to many high-resolution (HR) images. However, learning based SR algorithms are trained to map an LR image to the corresponding ground truth (GT) HR image in the training dataset. The training loss will increase and penalize the algorithm when the output does not exactly match the GT target, even when the outputs are mathematically valid candidates according to the SR framework. This becomes more problematic for the blind SR, as diverse unknown blur kernels exacerbate the ill-posedness of the problem. To this end, we propose a fundamentally different approach for the SR by introducing the concept of the adaptive target. The adaptive target is generated from the original GT target by a transformation to match the output of the SR network. The adaptive target provides an effective way for the SR algorithm to deal with the ill-posed nature of the SR, by providing the algorithm with the flexibility of accepting a variety of valid solutions. Experimental results show the effectiveness of our algorithm, especially for improving the perceptual quality of HR outputs.

关键词： Training computer vision Adaptive systems Impedance matching Superresolution Training data Network architecture

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 << < 471 472 473 474 475 476 477 478 479 480 > >>

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：