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

6,639 篇 会议
34 篇 期刊文献
5 册 图书

馆藏范围

6,677 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

3,950 篇 工学
- 3,725 篇 计算机科学与技术...
- 1,476 篇 软件工程
- 807 篇 光学工程
- 323 篇 信息与通信工程
- 240 篇 控制科学与工程
- 206 篇 机械工程
- 169 篇 电气工程
- 85 篇 生物医学工程（可授...
- 73 篇 电子科学与技术（可...
- 70 篇 生物工程
- 65 篇 仪器科学与技术
- 38 篇 建筑学
- 36 篇 土木工程
- 34 篇 力学（可授工学、理...
- 32 篇 航空宇航科学与技...
- 29 篇 安全科学与工程
- 23 篇 化学工程与技术
- 21 篇 材料科学与工程（可...
1,498 篇 理学
- 969 篇 物理学
- 929 篇 数学
- 369 篇 统计学（可授理学、...
- 136 篇 生物学
- 40 篇 系统科学
- 26 篇 化学
210 篇 医学
- 210 篇 临床医学
- 23 篇 基础医学(可授医学...
165 篇 管理学
- 123 篇 图书情报与档案管...
- 44 篇 管理科学与工程(可...
- 29 篇 工商管理
21 篇 法学
- 21 篇 社会学
10 篇 农学
9 篇 教育学
6 篇 经济学
2 篇 军事学
1 篇 艺术学

主题

2,364 篇 computer vision
848 篇 pattern recognit...
663 篇 cameras
634 篇 computer science
592 篇 face recognition
558 篇 layout
541 篇 conferences
527 篇 image segmentati...
514 篇 shape
454 篇 object recogniti...
453 篇 robustness
394 篇 humans
339 篇 feature extracti...
324 篇 training
305 篇 object detection
263 篇 image recognitio...
260 篇 application soft...
249 篇 lighting
248 篇 computational mo...
238 篇 image reconstruc...

机构

44 篇 microsoft resear...
27 篇 department of co...
21 篇 swiss fed inst t...
21 篇 school of comput...
21 篇 carnegie mellon ...
20 篇 department of co...
19 篇 swiss fed inst t...
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 center for autom...
14 篇 school of comput...
14 篇 school of comput...
13 篇 univ maryland co...
13 篇 microsoft resear...

作者

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

语言

6,668 篇 英文
8 篇 中文
2 篇 其他

检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是1091-1100 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：

Evaluating Scalable Bayesian Deep Learning Methods for Robust computer vision

Evaluating Scalable Bayesian Deep Learning Methods for Robus...

引用

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

作者： Gustafsson, Fredrik K. Danelljan, Martin Schoen, Thomas B. Uppsala Univ Dept Informat Technol Uppsala Sweden Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781728193601

While deep neural networks have become the go-to approach in computer vision, the vast majority of these models fail to properly capture the uncertainty inherent in their predictions. Estimating this predictive uncertainty can be crucial, for example in automotive applications. In Bayesian deep learning, predictive uncertainty is commonly decomposed into the distinct types of aleatoric and epistemic uncertainty. The former can be estimated by letting a neural network output the parameters of a certain probability distribution. Epistemic uncertainty estimation is a more challenging problem, and while different scalable methods recently have emerged, no extensive comparison has been performed in a real-world setting. We therefore accept this task and propose a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning. Our proposed framework is specifically designed to test the robustness required in real-world computer vision applications. We also apply this framework to provide the first properly extensive and conclusive comparison of the two current state-of-the-art scalable methods: ensembling and MC-dropout. Our comparison demonstrates that ensembling consistently provides more reliable and practically useful uncertainty estimates. Code is available at https://***/fregu856/evaluating_bdl.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

An OCR for Classical Indic Documents Containing Arbitrarily Long Words

An OCR for Classical Indic Documents Containing Arbitrarily ...

引用

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

作者： Dwivedi, Agam Saluja, Rohit Sarvadevabhatla, Ravi Kiran Hyderabad IIIT H Int Inst Informat Technol Ctr Visual Informat Technol CVIT Hyderabad 500032 India

ISBN: (纸本)9781728193601

OCR for printed classical Indic documents written in Sanskrit is a challenging research problem. It involves complexities such as image degradation, lack of datasets and long-length words. Due to these challenges, the word accuracy of available OCR systems, both academic and industrial, is not very high for such documents. To address these shortcomings, we develop a Sanskrit specific OCR system. We present an attention-based LSTM model for reading Sanskrit characters in line images. We introduce a dataset of Sanskrit document images annotated at line level. To augment real data and enable high performance for our OCR, we also generate synthetic data via curated font selection and rendering designed to incorporate crucial glyph substitution rules. Consequently, our OCR achieves a word error rate of 15.97% and a character error rate of 3.71% on challenging Indic document texts and outperforms strong baselines. Overall, our contributions set the stage for application of OCRs on large corpora of classic Sanskrit texts containing arbitrarily long and highly conjoined words.

关键词： Optical character recognition

来源：评论

学校读者我要写书评

暂无评论

CLEval: Character-Level Evaluation for Text Detection and recognition Tasks

CLEval: Character-Level Evaluation for Text Detection and Re...

引用

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

作者： Baek, Youngmin Nam, Daehyun Park, Sungrae Lee, Junyeop Shin, Seung Baek, Jeonghun Lee, Chae Young Lee, Hwalsuk NAVER Corp Clova AI Res Seongnam Si South Korea Yale Univ New Haven CT 06520 USA

ISBN: (纸本)9781728193601

Despite the recent success of text detection and recognition methods, existing evaluation metrics fail to provide a fair and reliable comparison among those methods. In addition, there exists no end-to-end evaluation metric that takes characteristics of OCR tasks into account. Previous end-to-end metric contains cascaded errors from the binary scoring process applied in both detection and recognition tasks. Ignoring partially correct results raises a gap between quantitative and qualitative analysis, and prevents fine-grained assessment. Based on the fact that character is a key element of text, we hereby propose a Character-Level Evaluation metric (CLEval). In CLEval, the instance matching process handles split and merge detection cases, and the scoring process conducts character-level evaluation. By aggregating character-level scores, the CLEval metric provides a fine-grained evaluation of end-to-end results composed of the detection and recognition as well as individual evaluations for each module from the end-performance perspective. We believe that our metrics can play a key role in developing and analyzing state-of-the-art text detection and recognition methods. The evaluation code is publicly available at https://***/clovaai/CLEval.

关键词： Character recognition

来源：评论

学校读者我要写书评

暂无评论

Reconstruct, Rasterize and Backprop: Dense shape and pose estimation from a single image

Reconstruct, Rasterize and Backprop: Dense shape and pose es...

引用

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

作者： Pokale, Aniket Aggarwal, Aditya Jatavallabhula, Krishna Murthy Krishna, K. Madhava IIIT Hyderabad Robot Res Ctr KCIS Hyderabad India Univ Montreal Mila Montreal PQ Canada

ISBN: (纸本)9781728193601

This paper presents a new system to obtain dense object reconstructions along with 6-DoF poses from a single image. Geared towards high fidelity reconstruction, several recent approaches leverage implicit surface representations and deep neural networks to estimate a 3D mesh of an object, given a single image. However, all such approaches recover only the shape of an object;the reconstruction is often in a canonical frame, unsuitable for downstream robotics tasks. To this end, we leverage recent advances in differentiable rendering (in particular, rasterization) to close the loop with 3D reconstruction in camera frame. We demonstrate that our approach-dubbed reconstruct, rasterize and backprop (RRB)-achieves significantly lower pose estimation errors compared to prior art, and is able to recover dense object shapes and poses from imagery. We further extend our results to an (offline) setup, where we demonstrate a dense monocular object-centric egomotion estimation system.

关键词： Three dimensional computer graphics

来源：评论

学校读者我要写书评

暂无评论

Optical Braille recognition Based on Semantic Segmentation Network with Auxiliary Learning Strategy

Optical Braille Recognition Based on Semantic Segmentation N...

引用

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

作者： Li, Renqiang Liu, Hong Wang, Xiangdong Xu, Jianxing Qian, Yueliang Chinese Acad Sci Inst Comp Technol Beijing Key Lab Mobile Comp & Pervas Device Beijing Peoples R China Northwestern Polytech Univ Xian Peoples R China

ISBN: (纸本)9781728193601

Optical Braille recognition methods usually use many designed steps, such as image de-skewing, Braille dots detection, Braille cell grids construction and Braille character recognition, which are less robust for complex Braille scenes. This paper proposes an optimal semantic segmentation framework BraUNet to directly detect and recognize Braille characters in the whole original Braille images. BraUNet adds extra auxiliary learning strategy to UNet network, which uses long-range connections of feature maps between encoder and decoder to get more low-level features. And auxiliary learning strategy can combine multi-class Braille characters segmentation with Braille foreground extraction, which can improve the feature learning ability and the Braille segmentation performance. Then morphological post-processing is used on semantic segmentation results to get the final individual Braille character regions. Experimental results show the proposed framework is robust, effective and fast for Braille characters segmentation and recognition on both complex double sided Braille image dataset and handwritten Braille image dataset.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing

Domain Agnostic Feature Learning for Image and Video Based F...

引用

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

作者： Saha, Suman Xu, Wenhao Kanakis, Menelaos Georgoulis, Stamatios Chen, Yuhua Paudel, Danda Pani Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Nowadays, the increasingly growing number of mobile and computing devices has led to a demand for safer user authentication systems. Face anti-spoofing is a measure towards this direction for biometric user authentication, and in particular face recognition, that tries to prevent spoof attacks. The state-of-the-art anti-spoofing techniques leverage the ability of deep neural networks to learn discriminative features, based on cues from the training set images or video samples, in an effort to detect spoof attacks. However, due to the particular nature of the problem, i.e. large variability due to factors like different backgrounds, lighting conditions, camera resolutions, spoof materials, etc., these techniques typically fail to generalize to new samples. In this paper, we explicitly tackle this problem and propose a class-conditional domain discriminator module, that, coupled with a gradient reversal layer, tries to generate live and spoof features that are discriminative, but at the same time robust against the aforementioned variability factors. Extensive experimental analysis shows the effectiveness of the proposed method over existing image- and video-based anti-spoofing techniques, both in terms of numerical improvement as well as when visualizing the learned features.

关键词： Face Lighting computer vision Authentication Feature extraction Cameras Visualization

来源：评论

学校读者我要写书评

暂无评论

SmallBigNet: Integrating Core and Contextual Views for Video Classification

SmallBigNet: Integrating Core and Contextual Views for Video...

引用

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

作者： Li, Xianhang Wang, Yali Zhou, Zhipeng Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit SIAT SenseTime Joint Lab Shenzhen Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc SIAT Branch Shenzhen Peoples R China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

Temporal convolution has been widely used for video classification. However, it is performed on spatio-temporal contexts in a limited view, which often weakens its capacity of learning video representation. To alleviate this problem, we propose a concise and novel SmallBig network, with the cooperation of small and big views. For the current time step, the small view branch is used to learn the core semantics, while the big view branch is used to capture the contextual semantics. Unlike traditional temporal convolution, the big view branch can provide the small view branch with the most activated video features from a broader 3D receptive field. Via aggregating such big-view contexts, the small view branch can learn more robust and discriminative spatio-temporal representations for video classification. Furthermore, we propose to share convolution in the small and big view branch, which improves model compactness as well as alleviates overfitting. As a result, our SmallBigNet achieves a comparable model size like 2D CNNs, while boosting accuracy like 3D CNNs. We conduct extensive experiments on the large-scale video benchmarks, e.g., Kinetics400, Something-Something VI and V2. Our SmallBig network outperforms a number of recent state-of-the-art approaches, in terms of accuracy and/or efficiency. The codes and models will be available on https://***/xhl-video/SmallBigNet .

关键词： Convolution Three-dimensional displays Two dimensional displays Electron tubes computer vision pattern recognition Semantics

来源：评论

学校读者我要写书评

暂无评论

Joint Learning of Blind Video Denoising and Optical Flow Estimation

Joint Learning of Blind Video Denoising and Optical Flow Est...

引用

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

作者： Yu, Songhyun Park, Bumjun Park, Junwoo Jeong, Jechang Hanyang Univ Seoul South Korea Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Many deep-learning-based image/video denoising models have been developed, and recently, several approaches for training a denoising neural network without using clean images have been proposed. However, Noise2Noise method requires paired noisy data, and obtaining them is occasionally difficult, whereas other existing models trained using unpaired noisy data deliver limited performance. Obtaining an accurate optical flow from noisy videos is also a difficult task because conventional optical flow estimation methods are primarily focused on estimating the optical flow using clean videos. This study proposes a new framework to fine-tune video denoising and optical flow estimation networks using unpaired noisy videos. These two networks are jointly trained to realize synergy;an improvement in the denoising performance increases the accuracy of the flow estimation, and an improvement in the flow-estimation performance enhances the quality of the training data for the denoiser. Our experimental results reveal that proposed approach outperforms the existing training schemes in video denoising and also provides accurate optical flows even when the videos contain a considerable amount of noise.

关键词： Videos Noise reduction Optical flow Noise measurement Estimation Training computer vision

来源：评论

学校读者我要写书评

暂无评论

Mimic The Raw Domain: Accelerating Action recognition in the Compressed Domain

Mimic The Raw Domain: Accelerating Action Recognition in the...

引用

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

作者： Battash, Barak Barad, Haim Tang, Hanlin Bleiweiss, Amit Intel Haifa Israel Intel Labs San Francisco CA USA

ISBN: (纸本)9781728193601

Video understanding usually requires expensive computation that prohibits its deployment, yet videos contain significant spatiotemporal redundancy that can be exploited. In particular, operating directly on the motion vectors and residuals in the compressed video domain can significantly accelerate the compute, by not using the raw videos which demand colossal storage capacity. Existing methods approach this task as a multiple modalities problem. In this paper we are approaching the task in a completely different way;we are looking at the data from the compressed stream as a one unit clip and propose that the residual frames can replace the original RGB frames from the raw domain. Furthermore, we are using teacher-student method to aid the network in the compressed domain to mimic the teacher network in the raw domain. We show experiments on three leading datasets (HMDB51, UCF1, and Kinetics) that approach state-of-the-art accuracy on raw video data by using compressed data. Our model MFCD-Net outperforms prior methods in the compressed domain and more importantly, our model has 11X fewer parameters and 3X fewer Flops, dramatically improving the efficiency of video recognition inference. This approach enables applying neural networks exclusively in the compressed domain without compromising accuracy while accelerating performance.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

Densely Self-guided Wavelet Network for Image Denoising

Densely Self-guided Wavelet Network for Image Denoising

引用

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

作者： Liu, Wei Yan, Qiong Zhao, Yuzhi SenseTime Res Hong Kong Peoples R China Harbin Inst Technol Harbin Heilongjiang Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

During the past years, deep convolutional neural networks have achieved impressive success in image denoising. In this paper, we propose a densely self-guided wavelet network (DSWN) for real-world image denoising. The basic structure of DSWN is a top-down self-guidance architecture which is able to efficiently incorporate multi- scale information and extract good local features to recover clean images. Moreover, such a structure requires a smaller number of parameters and enables us to achieve better effectiveness than Unet structure. To avoid information loss and achieve a better receptive field size, we embed wavelet transform into DSWN. In addition, we apply densely residual learning to convolution blocks to enhance the feature extraction capability of the proposed network. At the full resolution level of DSWN, we adopt a double branch structure to generate the final output. One branch of them tends to pay attention to dark areas and the other performs better on bright areas. Such a double branch strategy is able to handle the noise at different exposures. The proposed network is validated by BSD68, Kodak24 and SIDD+ benchmark. Additional experimental results show that the proposed network outperforms most state-of-the-art image denoising solutions.

关键词： computer vision pattern recognition conferences

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 << < 106 107 108 109 110 111 112 113 114 115 > >>

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：