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

5,349 篇 会议
128 册 图书
29 篇 期刊文献

馆藏范围

5,506 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,747 篇 工学
- 2,253 篇 计算机科学与技术...
- 926 篇 软件工程
- 478 篇 光学工程
- 399 篇 控制科学与工程
- 332 篇 机械工程
- 261 篇 仪器科学与技术
- 250 篇 信息与通信工程
- 128 篇 电气工程
- 122 篇 生物工程
- 90 篇 生物医学工程（可授...
- 70 篇 电子科学与技术（可...
- 39 篇 安全科学与工程
- 33 篇 化学工程与技术
- 31 篇 建筑学
- 27 篇 土木工程
- 25 篇 交通运输工程
- 24 篇 航空宇航科学与技...
858 篇 理学
- 526 篇 物理学
- 439 篇 数学
- 155 篇 统计学（可授理学、...
- 133 篇 生物学
- 28 篇 化学
- 21 篇 系统科学
242 篇 艺术学
- 242 篇 设计学（可授艺术学...
196 篇 管理学
- 101 篇 图书情报与档案管...
- 97 篇 管理科学与工程(可...
- 27 篇 工商管理
96 篇 医学
- 96 篇 临床医学
- 21 篇 基础医学(可授医学...
28 篇 法学
- 26 篇 社会学
8 篇 教育学
6 篇 经济学
5 篇 农学
2 篇 军事学

主题

2,165 篇 computer vision
915 篇 pattern recognit...
890 篇 conferences
640 篇 training
565 篇 cameras
410 篇 feature extracti...
373 篇 computational mo...
364 篇 image segmentati...
350 篇 visualization
338 篇 face recognition
321 篇 computer archite...
309 篇 robustness
290 篇 computer science
287 篇 shape
280 篇 humans
266 篇 object detection
234 篇 layout
196 篇 neural networks
191 篇 application soft...
191 篇 lighting

机构

20 篇 swiss fed inst t...
19 篇 institute for co...
18 篇 university of sc...
18 篇 microsoft resear...
18 篇 swiss fed inst t...
16 篇 carnegie mellon ...
16 篇 chinese acad sci...
14 篇 tsinghua univers...
14 篇 univ sci & techn...
14 篇 carnegie mellon ...
12 篇 harbin inst tech...
12 篇 tsinghua univ pe...
12 篇 department of co...
11 篇 megvii technol p...
11 篇 computer vision ...
11 篇 computer vision ...
10 篇 indian statistic...
10 篇 comp vis ctr bar...
10 篇 school of comput...
10 篇 univ modena & re...

作者

58 篇 timofte radu
17 篇 van gool luc
17 篇 luc van gool
17 篇 horst bischof
16 篇 rita cucchiara
16 篇 radu timofte
14 篇 escalera sergio
14 篇 bischof horst
14 篇 sergio escalera
13 篇 zhigang zhu
13 篇 hugo jair escala...
12 篇 murino vittorio
12 篇 li stan z.
12 篇 chen wei-ting
12 篇 qiang ji
11 篇 cucchiara rita
11 篇 fan haoqiang
11 篇 umapada pal
11 篇 marcos v. conde
11 篇 bowyer kevin w.

语言

5,492 篇 英文
10 篇 中文
5 篇 其他

检索条件"任意字段=2006 Conference on Computer Vision and Pattern Recognition Workshops"

共 5506 条记录，以下是4621-4630 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：

Rank-one Projections with Adaptive Margins for Face recognition

Rank-one Projections with Adaptive Margins for Face Recognit...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Dong Xu S. Lin Shuicheng Yan Xiaoou Tang Department of Electrical Engineering Columbia University USA MOE-Microsoft Key Laboratory of Multimedia Computing and Communication & Department of EEIS University of Science and Technology Hefei Anhui China Microsoft Research Asia Beijing China Department of Information Engineering Chinese University of Hong Kong Hong Kong China Beckman Institute University of Illinois Urbana-Champaign Champaign USA

In supervised dimensionality reduction, tensor representations of images have recently been employed to enhance classification of high-dimensional data with small training sets. To handle tensor data, this approach has been formulated with tight restrictions on projection directions that, along with convergence issues and the assumption of Gaussian distributed class data, limits its face recognition performance. To overcome these problems, we propose a method of rank-one projections with adaptive margins (RPAM) that gives a provably convergent solution for tensor data over a more general class of projections, while accounting for margins between samples of different classes. In contrast to previous margin based works which determine margin sample pairs within the original high dimensional space, RPAM instead aims to maximize the margins defined in the expected lower dimensional feature subspace by progressive margin refinement after each rank-one projection. In addition to handling tensor data, vector-based variants of RPAM are presented for linear mappings and for nonlinear mappings using kernel tricks. Comprehensive experimental results demonstrate that RPAM brings significant improvement in face recognition over previous subspace learning techniques.

关键词： Face recognition Tensile stress Linear discriminant analysis Asia computer vision pattern recognition Scattering Laboratories Multimedia computing Data engineering

来源：评论

学校读者我要写书评

暂无评论

Sparse and Semi-supervised Visual Mapping with the S^3GP

Sparse and Semi-supervised Visual Mapping with the S^3GP

引用

conference on computer vision and pattern recognition (CVPR)

作者： O. Williams A. Blake R. Cipolla University of Cambridge UK Microsoft Research UK Limited University of Cambridge Cambridgeshire UK

This paper is about mapping images to continuous output spaces using powerful Bayesian learning techniques. A sparse, semi-supervised Gaussian process regression model (S3GP) is introduced which learns a mapping using only partially labelled training data. We show that sparsity bestows efficiency on the S3GP which requires minimal CPU utilization for real-time operation; the predictions of uncertainty made by the S3GP are more accurate than those of other models leading to considerable performance improvements when combined with a probabilistic filter; and the ability to learn from semi-supervised data simplifies the process of collecting training data. The S3GP uses a mixture of different image features: this is also shown to improve the accuracy and consistency of the mapping. A major application of this work is its use as a gaze tracking system in which images of a human eye are mapped to screen coordinates: in this capacity our approach is efficient, accurate and versatile.

关键词： Training data Gaussian processes Uncertainty Robustness computer vision Bayesian methods Predictive models Filters Humans Runtime

来源：评论

学校读者我要写书评

暂无评论

TattTRN: Template Reconstruction Network for Tattoo Retrieval

TattTRN: Template Reconstruction Network for Tattoo Retrieva...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Lazaro Janier Gonzalez-Soler Maciej Salwowski Christian Rathgeb Daniel Fischer da/sec - Biometrics and Security Research Group Darmstadt Germany Faculty of Computer Science Technical University of Denmark Denmark

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Tattoos have been used effectively as soft biometrics to assist law enforcement in the identification of offenders and victims, as they contain discriminative information, and are a useful indicator to locate members of a criminal gang or organisation. Due to various privacy issues in the acquisition of images containing tattoos, only a limited number of databases exists. This lack of databases has delayed the development of new methods to effectively retrieve a potential suspect’s tattoo images from a candidate gallery. To mitigate this issue, in our work, we use an unsupervised generative approach to create a balanced database consisting of 28,550 semi-synthetic images with tattooed subjects from 571 tattoo categories. Further, we introduce a novel Tattoo Template Reconstruction Network (TattTRN), which learns to map the input tattoo sample to its respective tattoo template to enhance the distinguishing attributes of the final feature embedding. Experimental results with real data, i.e. WebTattoo and BIVTatt databases, demonstrate the soundness of the presented approach: an accuracy of up to 99% is achieved for checking at most the first 20 entries of the candidate list. 1

关键词： Image segmentation Data privacy computer vision Databases Law enforcement conferences Semantics

来源：评论

学校读者我要写书评

暂无评论

Recursive Joint Cross-Modal Attention for Multimodal Fusion in Dimensional Emotion recognition

Recursive Joint Cross-Modal Attention for Multimodal Fusion ...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： R. Gnana Praveen Jahangir Alam Computer Research Institute of Montreal (CRIM) Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Though multimodal emotion recognition has achieved significant progress over recent years, the potential of rich synergic relationships across the modalities is not fully exploited. In this paper, we introduce Recursive Joint Cross-Modal Attention (RJCMA) to effectively capture both intra- and inter-modal relationships across audio, visual, and text modalities for dimensional emotion recognition. In particular, we compute the attention weights based on cross-correlation between the joint audio-visual-text feature representations and the feature representations of individual modalities to simultaneously capture intra- and inter-modal relationships across the modalities. The attended features of the individual modalities are again fed as input to the fusion model in a recursive mechanism to obtain more refined feature representations. We have also explored Temporal Convolutional Networks (TCNs) to improve the temporal modeling of the feature representations of individual modalities. Extensive experiments are conducted to evaluate the performance of the proposed fusion model on the challenging Affwild2 dataset. By effectively capturing the synergic intra- and inter-modal relationships across audio, visual, and text modalities, the proposed fusion model achieves a Concordance Correlation Coefficient (CCC) of 0.585 (0.542) and 0.674 (0.619) for valence and arousal, respectively, on the validation set (test set). This shows a significant improvement over the baseline of 0.240 (0.211) and 0.200 (0.191) for valence and arousal, respectively, in the validation set (test set), achieving second place in the valence-arousal challenge of the 6th Affective Behavior Analysis in-the-Wild (ABAW) competition. The code is available on GitHub: https://***/praveena2j/RJCMA.

关键词： Correlation coefficient Visualization Emotion recognition computer vision Computational modeling conferences Refining

来源：评论

学校读者我要写书评

暂无评论

Coupled Bayesian Framework for Dual Energy Image Registration

Coupled Bayesian Framework for Dual Energy Image Registratio...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Hao Wu Yunqiang Chen Tong Fang University of Maryland MD USA Siemens AG Corporate Research and Development Princeton NJ USA

Image registration for X-ray dual energy imaging is challenging due to the overlaid transparent layers (i.e., the bone and soft tissue) and the different appearances between the dual images acquired with X-rays at different energy spectra. Moreover, subpixel accuracy is necessary for good reconstruction of the bone and soft-tissue layers. This paper addresses these problems with a novel coupled Bayesian framework, in which the registration and reconstruction can effectively reinforce each other. With the reconstruction results, we can design accurate matching criteria for aligning the dual images, instead of treating them as multi-modality registration. Furthermore, prior knowledge of the bone and soft tissue can be exploited to detect poor reconstruction due to inaccurate registration; and hence correct registration errors in the coupled framework. A multiscale freeform registration algorithm is implemented to achieve subpixel registration accuracy. Promising results are obtained in the experiments.

关键词： Bayesian methods Image registration Bones Biological tissues Image reconstruction X-ray imaging Optical imaging Attenuation Educational institutions computer vision

来源：评论

学校读者我要写书评

暂无评论

Orientation-aware Deep Neural Network for Real Image Super-Resolution

Orientation-aware Deep Neural Network for Real Image Super-R...

引用

IEEE/CVF conference on computer vision and pattern recognition workshops

作者： Chen Du He Zewei Sun Anshun Yang Jiangxin Cao Yanlong Cao Yanpeng Tang Siliang Michael Ying Yang School of Mechanical Engineering Zhejiang University School of Computer Science and Technology Zhejiang University SUG - Scene Understanding Group University of Twente

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Recently, Convolutional Neural Network (CNN) based approaches have achieved impressive single image super-resolution (SISR) performance in terms of accuracy and visual effects. It is noted that most SISR methods assume that the low-resolution (LR) images are obtained through bicubic interpolation down-sampling, thus their performance on real-world LR images is limited. In this paper, we proposed a novel orientation-aware deep neural network (OA-DNN) model, which incorporate a number of orientation feature extraction and channel attention modules (OAMs), to achieve good SR performance on real-world LR images captured by a single-lens reflex (DSLR) camera. Orientation-aware features extracted in different directions are adaptively combined through a channel-wise attention mechanism to generate more distinctive features for high-fidelity recovery of image details. Moreover, we reshape the input image into smaller spatial size but deeper depth via an inverse pixel-shuffle operation to accelerate the training/testing speed without sacrificing restoration accuracy. Extensive experimental results indicate that our OA-DNN model achieves a good balance between accuracy and speed. The extended OA-DNN*+ model further increases PSNR index by 0.18 dB compared with our previously submitted version. Codes will be made public after publication.

关键词： images super-resolution Neural network Visual effects real image Imagery (Psychotherapy) Feature extraction Image Single lens reflex digital SLR

来源：评论

学校读者我要写书评

暂无评论

DELTA: Decoupling Long-Tailed Online Continual Learning

DELTA: Decoupling Long-Tailed Online Continual Learning

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Siddeshwar Raghavan Jiangpeng He Fengqing Zhu School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

A significant challenge in achieving ubiquitous Artificial Intelligence is the limited ability of models to rapidly learn new information in real-world scenarios where data follows long-tailed distributions, all while avoiding forgetting previously acquired knowledge. In this work, we study the under-explored problem of Long-Tailed Online Continual Learning (LTOCL), which aims to learn new tasks from sequentially arriving class-imbalanced data streams. Each data is observed only once for training without knowing the task data distribution. We present DELTA, a decoupled learning approach designed to enhance learning representations and address the substantial imbalance in LTOCL. We enhance the learning process by adapting supervised contrastive learning to attract similar samples and repel dissimilar (out-of-class) samples. Further, by balancing gradients during training using an equalization loss, DELTA significantly enhances learning outcomes and successfully mitigates catastrophic forgetting. Through extensive evaluation, we demonstrate that DELTA improves the capacity for incremental learning, surpassing existing OCL methods. Our results suggest considerable promise for applying OCL in real-world applications. Code is available online 1

关键词： Continuing education Training computer vision Incremental learning conferences Contrastive learning Data models

来源：评论

学校读者我要写书评

暂无评论

A Framework for Feature Selection for Background Subtraction

A Framework for Feature Selection for Background Subtraction

引用

conference on computer vision and pattern recognition (CVPR)

作者： T. Parag A. Elgammal A. Mittal Department of Computer Science Rutgers University Piscataway NJ USA Real-time Vision & Modeling Department Siemens AG Corporate Research and Development Princeton NJ USA

Background subtraction is a widely used paradigm to detect moving objects in video taken from a static camera and is used for various important applications such as video surveillance, human motion analysis, etc. Various statistical approaches have been proposed for modeling a given scene background. However, there is no theoretical framework for choosing which features to use to model different regions of the scene background. In this paper we introduce a novel framework for feature selection for background modeling and subtraction. A boosting algorithm, namely RealBoost, is used to choose the best combination of features at each pixel. Given the probability estimates from a pool of features calculated by Kernel Density Estimate (KDE) over a certain time period, the algorithm selects the most useful ones to discriminate foreground objects from the scene background. The results show that the proposed framework successfully selects appropriate features for different parts of the image.

关键词： Layout Object detection Motion detection Cameras Video surveillance Humans Motion analysis Boosting Probability Kernel

来源：评论

学校读者我要写书评

暂无评论

Video Compressive Sensing with On-Chip Programmable Subsampling

Video Compressive Sensing with On-Chip Programmable Subsampl...

引用

IEEE conference on computer vision and pattern recognition workshops

作者： Leonidas Spinoulas Kuan He Oliver Cossairt Aggelos Katsaggelos Department of Electrical Engineering and Computer Science Northwestern University 2145 Sheridan Road Evanston IL 60208 USA

ISBN: (纸本)9781467367608

The maximum achievable frame-rate for a video camera is limited by the sensor's pixel readout rate. The same sensor may achieve either a slow frame-rate at full resolution (e.g., 60 fps at 4 Mpixel resolution) or a fast frame-rate at low resolution (e.g., 240 fps at 1 Mpixel resolution). Higher frame-rates are achieved using pixel readout modes (e.g., subsampling or binning) that sacrifice spatial for temporal resolution within a fixed bandwidth. A number of compressive video cameras have been introduced to overcome this fixed bandwidth constraint and achieve high frame-rates without sacrificing spatial resolution. These methods use electro-optic components (e.g., LCoS, DLPs, piezo actuators) to introduce high speed spatio-temporal multiplexing in captured images. Full resolution, high speed video is then restored by solving an undetermined system of equations using a sparse regularization framework. In this work, we introduce the first all-digital temporal compressive video camera that uses custom subsampling modes to achieve spatio-temporal multiplexing. Unlike previous compressive video cameras, ours requires no additional optical components, enabling it to be implemented in a compact package such as a mobile camera module. We demonstrate results using a True Sense development kit with a 12 Mpixel sensor and programmable FPGA read out circuitry.

关键词： Video Compressive Sensing On-Chip Programmable Subsampling sensor's pixel readout rate Video cameras bandwidth limitation readout Resolving power Compressed sensing PROGRAMMABLE Sensors System of equations temporal resolution actuators Videotapes MULTIPLEXOR system-on-chip

来源：评论

学校读者我要写书评

暂无评论

Online Multi-camera People Tracking with Spatial-temporal Mechanism and Anchor-feature Hierarchical Clustering

Online Multi-camera People Tracking with Spatial-temporal Me...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Riu Cherdchusakulchai Sasin Phimsiri Visarut Trairattanapa Suchat Tungjitnob Wasu Kudisthalert Pornprom Kiawjak Ek Thamwiwatthana Phawat Borisuitsawat Teepakorn Tosawadi Pakcheera Choppradit Kasisdis Mahakijdechachai Supawit Vatathanavaro Worawit Saetan Vasin Suttichaya AI and Robotics Ventures (ARV) Thailand

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-camera Multi-object tracking (MTMC) surpasses conventional single-camera tracking by enabling seamless object tracking across multiple camera views. This capability is critical for security systems and improving situational awareness in various environments. This paper proposes a novel MTMC framework designed for online operation. The framework employs a three-stage pipeline: Multiobject Tracking (MOT), Multi-target Multi-camera Tracking (MTMC), and Cross Interval Synchronization (CIS). In the MOT stage, ReID features are extracted and localized tracklets are created. MTMC links these tracklets across cameras using spatial-temporal constraints and constraint hierarchical clustering with anchor features for improved inter-camera association. Finally, CIS ensures the temporal coherence of tracklets across time intervals. The proposed framework achieves robust tracking performance, validated on the challenging 2024 AI City Challenge with a HOTA score of 51.0556%, ranking sixth. The code is available at: https://***/ARV-MLCORE/AIC2024Track1ARV

关键词： Urban areas Pipelines Cameras Feature extraction Real-time systems pattern recognition Synchronization

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 << < 459 460 461 462 463 464 465 466 467 468 > >>

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：