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

3,317 篇 会议
3 篇 期刊文献

馆藏范围

3,320 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,915 篇 工学
- 1,820 篇 计算机科学与技术...
- 376 篇 软件工程
- 142 篇 机械工程
- 136 篇 光学工程
- 42 篇 生物工程
- 28 篇 信息与通信工程
- 11 篇 控制科学与工程
- 9 篇 电气工程
- 9 篇 电子科学与技术（可...
- 9 篇 化学工程与技术
- 9 篇 交通运输工程
- 8 篇 生物医学工程（可授...
- 7 篇 安全科学与工程
- 4 篇 材料科学与工程（可...
- 4 篇 建筑学
- 3 篇 土木工程
- 3 篇 农业工程
174 篇 理学
- 136 篇 物理学
- 43 篇 生物学
- 29 篇 数学
- 16 篇 统计学（可授理学、...
- 10 篇 化学
29 篇 医学
- 28 篇 临床医学
- 3 篇 基础医学(可授医学...
15 篇 管理学
- 8 篇 管理科学与工程(可...
- 7 篇 图书情报与档案管...
- 3 篇 工商管理
5 篇 法学
- 3 篇 社会学
- 2 篇 法学
2 篇 教育学
- 2 篇 教育学
2 篇 农学
1 篇 经济学

主题

1,179 篇 computer vision
801 篇 conferences
570 篇 training
484 篇 pattern recognit...
330 篇 computational mo...
279 篇 computer archite...
255 篇 visualization
180 篇 feature extracti...
160 篇 neural networks
153 篇 semantics
152 篇 task analysis
145 篇 cameras
143 篇 deep learning
140 篇 three-dimensiona...
139 篇 benchmark testin...
126 篇 low-level vision
117 篇 vision
115 篇 language
113 篇 image segmentati...
112 篇 estimation

机构

35 篇 tsinghua univ pe...
34 篇 univ sci & techn...
28 篇 shanghai ai lab ...
26 篇 chinese univ hon...
25 篇 university of sc...
25 篇 tsinghua univers...
24 篇 peng cheng labor...
23 篇 swiss fed inst t...
23 篇 swiss fed inst t...
23 篇 peng cheng lab p...
22 篇 zhejiang univ pe...
22 篇 carnegie mellon ...
20 篇 univ chinese aca...
20 篇 university of ch...
18 篇 harbin inst tech...
18 篇 shanghai jiao to...
18 篇 sensetime res pe...
16 篇 hong kong univ s...
15 篇 fudan univ peopl...
14 篇 national key lab...

作者

60 篇 timofte radu
26 篇 radu timofte
19 篇 van gool luc
16 篇 lei lei
15 篇 loy chen change
15 篇 qiao yu
14 篇 fan haoqiang
13 篇 zhang yulun
13 篇 liu shuaicheng
13 篇 luc van gool
12 篇 chen chen
12 篇 chen wei-ting
12 篇 li chongyi
11 篇 wangmeng zuo
11 篇 pan jinshan
11 篇 marcos v. conde
10 篇 wei dong
10 篇 zhou shangchen
10 篇 suiyi zhao
10 篇 yu lei

语言

3,319 篇 英文
1 篇 其他

检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

共 3320 条记录，以下是3281-3290 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：

Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles

Deep-Learning-Based Aerial Image Classification for Emergenc...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Christos Kyrkou Theocharis Theocharides KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Unmanned Aerial Vehicles (UAVs), equipped with camera sensors can facilitate enhanced situational awareness for many emergency response and disaster management applications since they are capable of operating in remote and difficult to access areas. In addition, by utilizing an embedded platform and deep learning UAVs can autonomously monitor a disaster stricken area, analyze the image in real-time and alert in the presence of various calamities such as collapsed buildings, flood, or fire in order to faster mitigate their effects on the environment and on human population. To this end, this paper focuses on the automated aerial scene classification of disaster events from on-board a UAV. Specifically, a dedicated Aerial Image Database for Emergency Response (AIDER) applications is introduced and a comparative analysis of existing approaches is performed. Through this analysis a lightweight convolutional neural network (CNN) architecture is developed, capable of running efficiently on an embedded platform achieving ~3x higher performance compared to existing models with minimal memory requirements with less than 2% accuracy drop compared to the state-of-the-art. These preliminary results provide a solid basis for further experimentation towards real-time aerial image classification for emergency response applications using UAVs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Efficient and Accurate Face Alignment by Global Regression and Cascaded Local Refinement

Efficient and Accurate Face Alignment by Global Regression a...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jinzhan Su Zhe Wang Chunyuan Liao Haibin Ling Meitu Inc Hiscene Technology Temple University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Despite great advances witnessed on facial image alignment in recent years, high accuracy high speed face alignment algorithms still have rooms to improve especially for applications where computation resources are limited. Addressing this issue, we propose a new face landmark localization algorithm by combining global regression and local refinement. In particular, for a given image, our algorithm first estimates its global facial shape through a global regression network (GRegNet) and then using cascaded local refinement networks (LRefNet) to sequentially improve the alignment result. Compared with previous face alignment algorithms, our key innovation is the sharing of low level features in GRegNet with LRefNet. Such feature sharing not only significantly improves the algorithm efficiency, but also allows full exploration of rich locality-sensitive details carried with shallow network layers and consequently boosts the localization accuracy. The advantages of our algorithm is clearly validated in our thorough experiments on four popular face alignment benchmarks, 300-W, AFLW, COFW and WFLW. On all datasets, our algorithm produces state-of-the-art alignment accuracy, while enjoys the smallest computational complexity.

关键词： Shape Face Feature extraction Estimation Task analysis computer architecture Regression tree analysis

来源：评论

学校读者我要写书评

暂无评论

Leveraging Confident Points for Accurate Depth Refinement on Embedded Systems

Leveraging Confident Points for Accurate Depth Refinement on...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Fabio Tosi Matteo Poggi Stefano Mattoccia Department of Computer Science and Engineering (DISI) University of Bologna Italy

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Despite the notable progress in stereo disparity estimation, algorithms are still prone to errors in challenging conditions. Thus, heuristic disparity refinement techniques are usually deployed to improve accuracy. Moreover, state-of-the-art methods rely on complex CNNs requiring power hungry GPUs not suited for many practical applications constrained by limited computing resources. In this paper, we propose a novel technique for disparity refinement leveraging on confidence measures and a novel, automatic learning-based selection method to discard outliers. Then, a non-local strategy infers missing disparities by analyzing the closest reliable points. This framework is very fast and does not require any hand-tuned thresholding. We assess the performance of our Non-Local Anchoring (NLA), standalone refinement techniques and methods leveraging on confidence measures inside the stereo algorithm. Our evaluation with two popular stereo algorithms shows that our proposal significantly ameliorates their accuracy on Middlebury v3 and KITTI 2015 datasets. Moreover, since our method relies only on cues computed in the disparity domain, it is suited even for COTS stereo cameras coupled with embedded systems, e.g. nVidia Jetson TX2.

关键词： Reliability Proposals Pipelines Machine learning Embedded systems Forestry Task analysis

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning for Classifying Single Hand Gestures on Comprehensive Bharatanatyam Mudra Dataset

Transfer Learning for Classifying Single Hand Gestures on Co...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Anuja P. Parameshwaran Heta P. Desai Rajshekhar Sunderraman Michael Weeks Georgia State University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

For any dance form, either classical or folk, visual expressions - facial expressions and hand gestures play a key role in conveying the storyline of the accompanied music to the audience. Bharatanatyam - a classical dance form which has origins from the southern states of India, is on the verge of being completely automated partly due to an acute dearth of qualified and dedicated teachers/gurus. In an honest effort to speed up this automation process and at the same time preserve the cultural heritage, we have chosen to identify and classify the single hand gestures/mudras/hastas against their true labels by using two variations of the convolutional neural networks (CNNs) that demonstrates the exceeding effectiveness of transfer learning irrespective of the domain difference between the pre-training and the training dataset. This work is primarily aimed at 1) building a novel dataset of 2D single hand gestures belonging to 27 classes that were collected from Google search engine (Google images), YouTube videos (dynamic and with background considered) and professional artists under staged environment constraints (plain backgrounds), 2) exploring the effectiveness of Convolutional Neural Networks in identifying and classifying the single hand gestures by optimizing the hyperparameters, and 3) evaluating the impacts of transfer learning and double transfer learning, which is a novel concept explored in this paper for achieving higher classification accuracy.

关键词： Gesture recognition Assistive technology Training computer architecture Hidden Markov models Convolutional neural networks Google

来源：评论

学校读者我要写书评

暂无评论

Condensation-Net: Memory-Efficient Network Architecture With Cross-Channel Pooling Layers and Virtual Feature Maps

Condensation-Net: Memory-Efficient Network Architecture With...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tse-Wei Chen Motoki Yoshinaga Hongxing Gao Wei Tao Dongchao Wen Junjie Liu Kinya Osa Masami Kato Device Technology Development Headquarters Canon Inc. Tokyo Japan Canon Information Technology (Beijing) Co. Ltd. Haidian Beijing China

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Lightweight convolutional neural networks is an important research topic in the field of embedded vision. To implement image recognition tasks on a resource-limited hardware platform, it is necessary to reduce the memory size and the computational cost. The contribution of this paper is stated as follows. First, we propose an algorithm to process a specific network architecture (Condensation-Net) without increasing the maximum memory storage for feature maps. The architecture for virtual feature maps saves 26.5% of memory bandwidth by calculating the results of cross-channel pooling before storing the feature map into the memory. Second, we show that cross-channel pooling can improve the accuracy of object detection tasks, such as face detection, because it increases the number of filter weights. Compared with Tiny-YOLOv2, the improvement of accuracy is 2.0% for quantized networks and 1.5% for full-precision networks when the false-positive rate is 0.1. Last but not the least, the analysis results show that the overhead to support the cross-channel pooling with the proposed hardware architecture is negligible small. The extra memory cost to support Condensation-Net is 0.2% of the total size, and the extra gate count is only 2.1% of the total size.

关键词： Hardware computer architecture Network architecture Training Microsoft Windows Feature extraction Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Deep Anchored Convolutional Neural Networks

Deep Anchored Convolutional Neural Networks

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jiahui Huang Kshitij Dwivedi Gemma Roig Singapore University of Technology and Design

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Convolutional Neural Networks (CNNs) have been proven to be extremely successful at solving computer vision tasks. State-of-the-art methods favor such deep network architectures for its accuracy performance, with the cost of having massive number of parameters and high weights redundancy. Previous works have studied how to prune such CNNs weights. In this paper, we go to another extreme and analyze the performance of a network stacked with a single convolution kernel across layers, as well as other weights sharing techniques. We name it Deep Anchored Convolutional Neural Network (DACNN). Sharing the same kernel weights across layers allows to reduce the model size tremendously, more precisely, the network is compressed in memory by a factor of L, where L is the desired depth of the network, disregarding the fully connected layer for prediction. The number of parameters in DACNN barely increases as the network grows deeper, which allows us to build deep DACNNs without any concern about memory costs. We also introduce a partial shared weights network (DACNN-mix) as well as an easy-plug-in module, coined regulators, to boost the performance of our architecture. We validated our idea on 3 datasets: CIFAR-10, CIFAR-100 and SVHN. Our results show that we can save massive amounts of memory with our model, while maintaining a high accuracy performance.

关键词： Convolution Kernel Regulators computer architecture Task analysis Neural networks Adaptation models

来源：评论

学校读者我要写书评

暂无评论

Single Image Based Metric Learning via Overlapping Blocks Model for Person Re-Identification

Single Image Based Metric Learning via Overlapping Blocks Mo...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yipeng Chen Cairong Zhao Tianli Sun Department of Computer Science and Technology Tongji University Shanghai China

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Considering the pedestrian structure characteristics, the first step of many person re-identification algorithms is to divide the pedestrian images or feature map into several blocks, and then the blocks in the same location are used to calculate the special loss functions that metric the differences between different images, to reduce the distance between intra-samples and to increase the distance between inter-samples. However, most of those blocks based deep metric learning methods only measure the difference between different images, but ignored the metrics between different blocks in a single image. In this paper, we propose a novel blocks based method for person re-identification called Overlapping Blocks Model (OBM), in which an innovative strategy of overlapping partition on convolutional features is used to construct multiple overlapping blocks structure and a novel overlapping blocks loss function is utilized to measure the difference between different blocks in a single image, to ensure more blocks can bring more discriminate information and higher performance. We conduct thorough validation experiments on the Market-1501, CUHK03, and DukeMTMC-reID datasets, which demonstrate that our proposed Overlapping Blocks Model can effectively improve the recognition performance of networks by adding the multiple overlapping blocks structure and the overlapping blocks loss.

关键词： Learning systems Loss measurement Training Tensile stress Feature extraction Correlation

来源：评论

学校读者我要写书评

暂无评论

Robust Visual Tracking via Collaborative and Reinforced Convolutional Feature Learning

Robust Visual Tracking via Collaborative and Reinforced Conv...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Dongdong Li Yangliu Kuai Gongjian Wen Li Liu College of Electronic Science and Technology National University of Defense Technology China College of System Engineering National University of Defense Technology China Center for Machine Vision and Signal Analysis University of Oulu Finland

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Convolutional neural networks are potent models that yield hierarchies of features and have drawn increasing interest in the visual tracking field. In the paper, we design an end-to-end trainable tracking framework based on Siamese network, which proposes to learn the low-level fine-grained and high-level semantic representations simultaneously with the aim of mutual benefit. Due to the distinct and complementary characteristics of the feature hierarchies, different tracking mechanisms are adopted for different feature layers. The low-level features are exploited and updated with a correlation filter layer for adaptive tracking and the high-level features are compared through cross-correlation directly for robust tracking. The two-level features are jointly trained with a multi-task loss function end-to-end. The proposed tracker takes full advantage of the adaptability of the low-level features and the generalization ability of the high-level features. Extensive experimental tracking results on the widely used OTB and TC128 benchmarks demonstrate the superiority of our tracker. Meanwhile, our proposed tracker can achieve a real-time tracking speed.

关键词： Target tracking Correlation Semantics Feature extraction Robustness Visualization Real-time systems

来源：评论

学校读者我要写书评

暂无评论

2D-3D Heterogeneous Face recognition Based on Deep Coupled Spectral Regression

2D-3D Heterogeneous Face Recognition Based on Deep Coupled S...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yangtao Zheng Di Huang Weixin Li Shupeng Wang Yunhong Wang IRIP Lab School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

As one of the major branches in Face recognition (FR), 2D-3D Heterogeneous FR (HFR), where face comparison is achieved across the texture and shape modalities, has become more important. This paper proposes a novel deep learning based end-to-end approach, namely Deep Coupled Spectral Regression (DCSR), for such an issue. It jointly makes use of both the advantages of CNN based deep features and CSR based common subspace. Specifically, from 2D texture and 3D depth face maps, DCSR extracts more powerful features by a deep network with the cross-modality triplet loss, which show much better uniqueness and robustness than the hand-crafted ones. Further, DCSR learns the shared space between different modalities with the constraints of sample labels, and is thereby more discriminative than the widely used unsupervised methods. More importantly, the two steps above are integrated through a couple layer to explicitly optimize the weights of deep features and projection directions rather than a simple combination. Experiments are carried out on the FRGC v2.0 database, and the results reported clearly demonstrate the competency of our proposed method. Its generalization ability is also validated by additional experiments conducted on the CASIA NIR-VIS 2.0 database.

关键词： Face Three-dimensional displays Two dimensional displays Feature extraction Shape Databases Lighting

来源：评论

学校读者我要写书评

暂无评论

Multiscale Kernels for Enhanced U-Shaped Network to Improve 3D Neuron Tracing

Multiscale Kernels for Enhanced U-Shaped Network to Improve ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Heng Wang Donghao Zhang Yang Song Siqi Liu Heng Huang Mei Chen Hanchuan Peng Weidong Cai School of Computer Science University of Sydney Australia School of Computer Science and Engineering University of New South Wales Australia Digital Services Digital Technology & Innovation Siemens Healthineers USA Department of Electrical and Computer Engineering University of Pittsburgh USA Department of Informatics University of Albany State University of New York USA Allen Institute for Brain Science USA

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Digital neuron morphology reconstruction from three-dimensional (3D) volumetric optical microscope images is an important procedure to rebuild the connections and structures of neural circuits. Even though many approaches have been proposed to achieve precise tracing, it is still a challenging task especially when images are polluted by noise or have discontinuity in their neuron structures. In this paper, we propose a new framework to overcome these issues by performing neuron segmentation prior to tracing. Our proposed framework adopts a novel 3D U-shaped convolutional neural network (CNN) with multiscale kernel fusion and spatial fusion to perform the image segmentation. We then perform the iterative back-tracking tracing algorithm on the output of the network. Evaluated on the Janelia dataset from the BigNeuron project, our proposed framework achieves competitive tracing performance.

关键词： Neurons Three-dimensional displays Image segmentation Kernel Gold Optical imaging Standards

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332页 << < 323 324 325 326 327 328 329 330 331 332 > >>

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：