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

3,854 篇 会议
1 篇 期刊文献

馆藏范围

3,855 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,938 篇 工学
- 2,846 篇 计算机科学与技术...
- 210 篇 软件工程
- 141 篇 机械工程
- 134 篇 光学工程
- 43 篇 生物工程
- 28 篇 信息与通信工程
- 10 篇 控制科学与工程
- 10 篇 交通运输工程
- 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
- 7 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
1,540 篇 医学
- 1,540 篇 临床医学
176 篇 理学
- 137 篇 物理学
- 44 篇 生物学
- 30 篇 数学
- 17 篇 统计学（可授理学、...
- 10 篇 化学
- 2 篇 大气科学
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...
- 4 篇 工商管理
6 篇 法学
- 4 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

2,497 篇 computer vision
1,153 篇 pattern recognit...
1,102 篇 training
834 篇 conferences
688 篇 computational mo...
531 篇 visualization
465 篇 computer archite...
429 篇 benchmark testin...
398 篇 semantics
377 篇 three-dimensiona...
362 篇 codes
307 篇 feature extracti...
297 篇 task analysis
296 篇 deep learning
262 篇 neural networks
226 篇 image segmentati...
221 篇 estimation
218 篇 object detection
212 篇 cameras
205 篇 measurement

机构

50 篇 univ sci & techn...
47 篇 tsinghua univ pe...
46 篇 peng cheng lab p...
42 篇 zhejiang univ pe...
41 篇 chinese univ hon...
39 篇 stanford univ st...
38 篇 swiss fed inst t...
38 篇 sensetime res pe...
37 篇 carnegie mellon ...
36 篇 swiss fed inst t...
33 篇 natl univ singap...
32 篇 yonsei univ
32 篇 shanghai jiao to...
29 篇 univ chinese aca...
29 篇 korea adv inst s...
28 篇 megvii technol p...
28 篇 univ hong kong p...
28 篇 tech univ munich...
27 篇 alibaba grp peop...
26 篇 australian natl ...

作者

68 篇 timofte radu
25 篇 van gool luc
23 篇 zhang lei
21 篇 sun jian
19 篇 radu timofte
17 篇 yang yi
16 篇 tao dacheng
15 篇 loy chen change
14 篇 fan haoqiang
14 篇 wang xinchao
14 篇 liu shuaicheng
14 篇 lee kyoung mu
14 篇 ji rongrong
13 篇 huang feiyue
13 篇 zhang wei
12 篇 li xin
12 篇 liu yang
12 篇 wang kai
12 篇 pan jinshan
12 篇 lei lei

语言

3,855 篇 英文

检索条件"任意字段=2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021"

共 3855 条记录，以下是3781-3790 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：

Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification

Solo or Ensemble? Choosing a CNN Architecture for Melanoma C...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Fabio Perez Sandra Avila Eduardo Valle School of Electrical and Computing Engineering (FEEC) Institute of Computing (IC) RECOD Lab University of Campinas (UNICAMP)

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Convolutional neural networks (CNNs) deliver exceptional results for computer vision, including medical image analysis. With the growing number of available architectures, picking one over another is far from obvious. Existing art suggests that, when performing transfer learning, the performance of CNN architectures on ImageNet correlates strongly with their performance on target tasks. We evaluate that claim for melanoma classification, over 9 CNNs architectures, in 5 sets of splits created on the ISIC Challenge 2017 dataset, and 3 repeated measures, resulting in 135 models. The correlations we found were, to begin with, much smaller than those reported by existing art, and disappeared altogether when we considered only the top-performing networks: uncontrolled nuisances (i.e., splits and randomness) overcome any of the analyzed factors. Whenever possible, the best approach for melanoma classification is still to create ensembles of multiple models. We compared two choices for selecting which models to ensemble: picking them at random (among a pool of high-quality ones) vs. using the validation set to determine which ones to pick first. For small ensembles, we found a slight advantage on the second approach but found that random choice was also competitive. Although our aim in this paper was not to maximize performance, we easily reached AUCs comparable to the first place on the ISIC Challenge 2017.

关键词： Ensembles ARCHITECTURE Melanoma image analysis Taxonomy Picking images computer vision Random Number Generators Arts Claim Choose Random

来源：评论

学校读者我要写书评

暂无评论

Low Rank Poisson Denoising (LRPD): A Low Rank Approach using split Bregman Algorithm for Poisson Noise Removal from Images

Low Rank Poisson Denoising (LRPD): A Low Rank Approach using...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Prashanth Kumar G. Rajiv Ranjan Sahay Computational Vision Laboratory Advanced Technology Development Centre Indian Institute of Technology Kharagpur Department of Electrical Engineering Department of Electrical Engineering Indian Institute of Technology Kharagpur

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Occurrence of Poisson noise in captured observations is inevitable in various real imaging applications ranging from medical imaging to night vision imaging. Restoration of fine details of an image is difficult when it is corrupted by Poisson noise. Recently, low rank approaches outperformed several state-of-the-art techniques for image denoising, deblurring, image completion, super-resolution, etc. The ability of low rank techniques to preserve fine details, even though the image is corrupted by severe noise, motivated us to develop an optimization framework wherein, we propose to use a low rank prior for Poisson noise removal. In the proposed low rank Poisson denoising (LRPD) algorithm, we resort to split Bregman technique to solve an appropriate objective function. We incorporate the forward-backward splitting scheme to minimize the first subproblem and the weighted nuclear norm minimization (WNNM) for the second subproblem of split Bregman algorithm to arrive at the final solution. We conduct several experiments on both simulated and real-world Poisson noisy data and show the superiority of the proposed method over other state-of-the-art Poisson denoising techniques.

关键词： Poisson noise images Rank of data Imagery (Psychotherapy) Sim'eon Denis Poisson Noise reduction Night vision image denoising Noise cancellation Objective function

来源：评论

学校读者我要写书评

暂无评论

DSCnet: Replicating Lidar Point Clouds with Deep Sensor Cloning

DSCnet: Replicating Lidar Point Clouds with Deep Sensor Clon...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Paden Tomasello Sammy Sidhu Anting Shen Matthew W. Moskewicz Nobie Redmon Gayatri Joshi Romi Phadte Paras Jain Forrest Iandola DeepScale Inc

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Convolutional neural networks (CNNs) have become increasingly popular for solving a variety of computer vision tasks, ranging from image classification to image segmentation. Recently, autonomous vehicles have created a demand for depth information, which is often obtained using hardware sensors such as Light detection and ranging (LIDAR). Although it can provide precise distance measurements, most LIDARs are still far too expensive to sell in mass-produced consumer vehicles, which has motivated methods to generate depth information from commodity automotive sensors like cameras. In this paper, we propose an approach called Deep Sensor Cloning (DSC). The idea is to use Convolutional Neural Networks in conjunction with inexpensive sensors to replicate the 3D point-clouds that are created by expensive LIDARs. To accomplish this, we develop a new dataset (DSC-data) and a new family of CNN architectures (DSCnets). While previous tasks such as KITTI depth prediction use interpolated RGB-D images as ground-truth for training, we instead use DSCnets to directly predict LIDAR point-clouds. When we compare the output of our models to a $75,000 LIDAR, we find that our most accurate DSCnet achieves a relative error of 5.77% using a single camera and 4.69% using stereo cameras.

关键词： Lidar Sensors Stereo cameras Cameras ground truth images LIght Detection and Ranging Image classification Cloning New families relative errors image segmentation Replicating computer vision distance measurement

来源：评论

学校读者我要写书评

暂无评论

DHP19: Dynamic vision Sensor 3D Human Pose Dataset

DHP19: Dynamic Vision Sensor 3D Human Pose Dataset

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Enrico Calabrese Gemma Taverni Christopher Awai Easthope Sophie Skriabine Federico Corradi Luca Longinotti Kynan Eng Tobi Delbruck Institute of Neuroinformatics University of Zurich and ETH Zurich Balgrist University Hospital University of Zurich iniVation AG

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Human pose estimation has dramatically improved thanks to the continuous developments in deep learning. However, marker-free human pose estimation based on standard frame-based cameras is still slow and power hungry for real-time feedback interaction because of the huge number of operations necessary for large Convolutional Neural Network (CNN) inference. Event-based cameras such as the Dynamic vision Sensor (DVS) quickly output sparse moving-edge information. Their sparse and rapid output is ideal for driving low-latency CNNs, thus potentially allowing real-time interaction for human pose estimators. Although the application of CNNs to standard frame-based cameras for human pose estimation is well established, their application to event-based cameras is still under study. This paper proposes a novel benchmark dataset of human body movements, the Dynamic vision Sensor Human Pose dataset (DHP19). It consists of recordings from 4 synchronized 346x260 pixel DVS cameras, for a set of 33 movements with 17 subjects. DHP19 also includes a 3D pose estimation model that achieves an average 3D pose estimation error of about 8 cm, despite the sparse and reduced input data from the DVS.

关键词： Dataset pose estimation Human Daphne virus S vision sensors Cameras Estimators

来源：评论

学校读者我要写书评

暂无评论

FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing

FeatherNets: Convolutional Neural Networks as Light as Feath...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Peng Zhang Fuhao Zou Zhiwen Wu Nengli Dai Skarpness Mark Michael Fu Juan Zhao Kai Li Intel IAGS SSP School of Computer Science and Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Face Anti-spoofing gains increased attentions recently in both academic and industrial fields. With the emergence of various CNN based solutions, the multi-modal (RGB, depth and IR) methods based CNN showed better performance than single modal classifiers. However, there is a need for improving the performance and reducing the complexity. Therefore, an extreme light network architec-ture (FeatherNet A/B) is proposed with a streaming module which fixes the weakness of Global Average Pooling and uses less parameters. Our single FeatherNet trained by depth image only, provides a higher baseline with 0.00168 ACER, 0.35M parameters and 83M FLOPS. Furthermore, a novel fusion procedure with "ensemble + cascade" structure is presented to satisfy the performance preferred use cases. Meanwhile, the MMFD dataset is collected to provide more attacks and diversity to gain better generalization. We use the fusion method in the Face Anti-spoofing Attack Detection Challenge@CVPR2019 and got the result of 0.0013 (ACER), 0.999 (TPR@FPR=10e-2), 0.998 (TPR@FPR =10e-3) and 0.9814 (TPR@FPR=10e-4).

关键词： Feathers fusion method Light fusion procedure Face preferred use Hypodynamia Neural network Emergence

来源：评论

学校读者我要写书评

暂无评论

Multi-Object Portion Tracking in 4D Fluorescence Microscopy Imagery with Deep Feature Maps

Multi-Object Portion Tracking in 4D Fluorescence Microscopy ...

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Yang Jiao Mo Weng Mei Yang Department of Electrical and Computer Engineering University of Nevada School of Life Science University of Nevada

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

3D fluorescence microscopy of living organisms has increasingly become an essential and powerful tool in biomedical research and diagnosis. An exploding amount of imaging data has been collected, whereas efficient and effective computational tools to extract information from them are still lagging behind. This is largely due to the challenges in analyzing biological data. Interesting biological structures are not only small, but are often morphologically irregular and highly dynamic. Although tracking cells in live organisms has been studied for years, existing tracking methods for cells are not effective in tracking subcellular structures, such as protein complexes, which feature in continuous morphological changes including split and merge, in addition to fast migration and complex motion. In this paper, we first define the problem of multi-object portion tracking to model the protein object tracking process. A multi-object tracking method with portion matching is proposed based on 3D segmentation results. The proposed method distills deep feature maps from deep networks, then recognizes and matches objects' portions using an extended search. Experimental results confirm that the proposed method achieves 2.96% higher on consistent tracking accuracy and 35.48% higher on event identification accuracy than the state-of-art methods.

关键词： Organism feature map tracking Fluorescence microscopy Biomedical Research four-dimensional

来源：评论

学校读者我要写书评

暂无评论

Benchmarking Gaze Prediction for Categorical Visual Search

Benchmarking Gaze Prediction for Categorical Visual Search

引用

ieee/cvf conference on computer vision and pattern recognition workshops

作者： Gregory Zelinsky Zhibo Yang Lihan Huang Yupei Chen Seoyoung Ahn Zijun Wei Hossein Adeli Dimitris Samaras Minh Hoai Stony Brook University

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

The prediction of human shifts of attention is a widely-studied question in both behavioral and computer vision, especially in the context of a free viewing task. However, search behavior, where the fixation scanpaths are highly dependent on the viewer's goals, has received far less attention, even though visual search constitutes much of a person's everyday behavior. One reason for this is the absence of real-world image datasets on which search models can be trained. In this paper we present a carefully created dataset for two target categories, microwaves and clocks, curated from the COCO2014 dataset. A total of 2183 images were presented to multiple participants, who were tasked to search for one of the two categories. This yields a total of 16184 validated fixations used for training, making our microwave-clock dataset currently one of the largest datasets of eye fixations in categorical search. We also present a 40-image testing dataset, where images depict both a microwave and a clock target. Distinct fixation patterns emerged depending on whether participants searched for a microwave (n=30) or a clock (n=30) in the same images, meaning that models need to predict different search scanpaths from the same pixel inputs. We report the results of several state-of-the-art deep network models that were trained and evaluated on these datasets. Collectively, these datasets and our protocol for evaluation provide what we hope will be a useful test-bed for the development of new methods for predicting category-specific visual search behavior.

关键词： Visual Search Timing devices Dataset Ocular Fixation images MICROWAVE Categorical Nominal computer vision test bed new methods searching behavior

来源：评论

学校读者我要写书评

暂无评论

SparsePPG: Towards Driver Monitoring Using Camera-Based Vital Signs Estimation in Near-Infrared 31

SparsePPG: Towards Driver Monitoring Using Camera-Based Vita...

引用

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

作者： Nowara, Ewa Magdalena Marks, Tim K. Mansour, Hassan Veeraraghavan, Ashok MERL Cambridge MA 02139 USA Rice Univ Houston TX 77005 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Camera-based measurement of the heartbeat signal from minute changes in the appearance of a person's skin is known as remote photoplethysmography (rPPG). Methods for rPPG have improved considerably in recent years, making possible its integration into applications such as telemedicine. Driver monitoring using in-car cameras is another potential application of this emerging technology. Unfortunately, there are several challenges unique to the driver monitoring context that must be overcome. First, there are drastic illumination changes on the driver's face, both during the day (as sun filters in and out of overhead trees, etc.) and at night (from streetlamps and oncoming headlights), which current rPPG algorithms cannot account for: We argue that these variations are significantly reduced by narrow-bandwidth near-infrared (NIR) active illumination at 940 nm, with matching bandpass filter on the camera. Second, the amount of motion during driving is significant. We perform a preliminary analysis of the motion magnitude and argue that any in-car solution must provide better robustness to motion artifacts. Third, low signal-tonoise ratio (SNR) and false peaks due to motion have the potential to confound the rPPG signal. To address these challenges, we develop a novel rPPG signal tracking and denoising algorithm (sparsePPG) based on Robust Principal Components Analysis and sparse frequency spectrum estimation. We release a new dataset of face videos collected simultaneously in RGB and NIR. We demonstrate that in each of these frequency ranges, our new method performs as well as or better than current state-of-the-art rPPG algorithms. Overall, our preliminary study indicates that while driver vital signs monitoring using cameras is promising, much work needs to be done in terms of improving robustness to motion artifacts before it becomes practical.

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

IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded vision 31

IFQ-Net: Integrated Fixed-point Quantization Networks for Em...

引用

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

作者： Gao, Hongxing Tao, Wei Wen, Dongchao Chen, Tse-Wei Osa, Kinya Kato, Masami Canon Informat Technol Beijing Co LTD Beijing Peoples R China Canon Inc Device Technol Dev Headquarters Tokyo Japan

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Deploying deep models on embedded devices has been a challenging problem since the great success of deep learning based networks. Fixed-point networks, which represent their data with low bits fixed-point and thus give remarkable savings on memory usage, are generally preferred. Even though current fixed-point networks employ relative low bits (e.g. 8-bits), the memory saving is far from enough for the embedded devices. On the other hand, quantization deep networks, for example XNOR-Net and HWGQ-Net, quantize the data into 1 or 2 bits resulting in more significant memory savings but still contain lots of floating-point data. In this paper, we propose a fixed-point network for embedded vision tasks through converting the floating-point data in a quantization network into fixed-point. Furthermore, to overcome the data loss caused by the conversion, we propose to compose floating-point data operations across multiple layers (e.g. convolution, batch normalization and quantization layers) and convert them into fixedpoint. We name the fixed-point network obtained through such integrated conversion as Integrated Fixed-point Quantization Networks (IFQ-Net). We demonstrate that our IFQ-Net gives 2.16x and 18x more savings on model size and runtime feature map memory respectively with similar accuracy on ImageNet. Furthermore, based on YOLOv2, we design IFQ-Tinier-YOLO face detector which is a fixed-point network with 256x reduction in model size (246k Bytes) than Tiny-YOLO. We illustrate the promising performance of our face detector in terms of detection rate on Face Detection Data Set and Bencmark (FDDB) and qualitative results of detecting small faces of Wider Face dataset.

关键词： Quantization (signal) Convolution Face Computational modeling Task analysis Detectors Runtime

来源：评论

学校读者我要写书评

暂无评论

Time Analysis of Pulse-based Face Anti-Spoofing in Visible and NIR 31

Time Analysis of Pulse-based Face Anti-Spoofing in Visible a...

引用

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

作者： Hernandez-Ortega, Javier Fierrez, Julian Morales, Aythami Tome, Pedro Univ Autonoma Madrid Biometr & Data Pattern Analyt BiDA Lab Madrid Spain

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper we study Presentation Attack Detection (PAD) in face recognition systems against realistic artifacts such as 3D masks or good quality of photo attacks. In recent works, pulse detection based on remote photo-plethysmography (rPPG) has shown to be a effective countermeasure in concrete setups, but still there is a need for a deeper understanding of when and how this kind of PAD works in various practical conditions. Related works analyze full video sequences (usually over 60 seconds) to distinguish between attacks and legitimate accesses. However, existing approaches may not be as effective as it has been claimed in the literature in time variable scenarios. In this paper we evaluate the performance of an existent state-of-the-art PAD scheme based on rPPG when analyzing short-time video sequences extracted from a longer video. Results are reported using the 3D Mask Attack Database (3DMAD), and a self-collected dataset called Heart Rate Database (HR), including different video durations, spectrum bands, resolutions and frame rates. Several conclusions can be drawn from this work: a) PAD performance based on rPPG varies significantly with the length of the analyzed video, b) rPPG information extracted from short-time sequences (over 5 seconds) can be discriminant enough for performing the PAD task, c) in general, videos using the NIR band perform better than those using the RGB band, and d) the temporal resolution is more valuable for rPPG signal extraction than the spatial resolution.

关键词： Face Three-dimensional displays Heart rate Databases Face recognition Video sequences Feature extraction

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386页 << < 375 376 377 378 379 380 381 382 383 384 > >>

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：