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

50,479 篇 会议
1,421 册 图书
1,041 篇 期刊文献
1 篇 学位论文

馆藏范围

52,940 篇 电子文献
4 种 纸本馆藏

日期分布

学科分类号

31,811 篇 工学
- 24,804 篇 计算机科学与技术...
- 12,568 篇 软件工程
- 5,153 篇 光学工程
- 4,756 篇 电气工程
- 4,436 篇 信息与通信工程
- 4,257 篇 机械工程
- 3,956 篇 控制科学与工程
- 2,474 篇 生物工程
- 1,728 篇 生物医学工程（可授...
- 1,584 篇 仪器科学与技术
- 1,317 篇 电子科学与技术（可...
- 793 篇 化学工程与技术
- 698 篇 安全科学与工程
- 542 篇 交通运输工程
- 379 篇 建筑学
- 331 篇 土木工程
11,839 篇 理学
- 6,434 篇 物理学
- 5,405 篇 数学
- 2,761 篇 生物学
- 1,910 篇 统计学（可授理学、...
- 801 篇 化学
- 669 篇 系统科学
5,305 篇 医学
- 5,094 篇 临床医学
- 729 篇 基础医学(可授医学...
- 459 篇 药学(可授医学、理...
3,350 篇 管理学
- 1,953 篇 图书情报与档案管...
- 1,535 篇 管理科学与工程(可...
- 479 篇 工商管理
720 篇 艺术学
- 718 篇 设计学（可授艺术学...
428 篇 法学
- 401 篇 社会学
297 篇 农学
197 篇 教育学
163 篇 经济学
63 篇 文学
49 篇 军事学

主题

17,385 篇 computer vision
9,017 篇 pattern recognit...
4,196 篇 training
3,815 篇 feature extracti...
3,134 篇 cameras
2,870 篇 computational mo...
2,789 篇 image segmentati...
2,622 篇 visualization
2,573 篇 shape
2,533 篇 face recognition
2,171 篇 robustness
2,123 篇 computer science
1,973 篇 object detection
1,959 篇 computer archite...
1,878 篇 layout
1,853 篇 object recogniti...
1,802 篇 three-dimensiona...
1,725 篇 neural networks
1,708 篇 humans
1,691 篇 image recognitio...

机构

165 篇 univ chinese aca...
144 篇 tsinghua univers...
136 篇 national laborat...
108 篇 univ sci & techn...
104 篇 zhejiang univers...
100 篇 shanghai jiao to...
95 篇 microsoft resear...
94 篇 university of sc...
86 篇 zhejiang univ pe...
84 篇 shanghai ai lab ...
74 篇 school of comput...
69 篇 computer vision ...
68 篇 peking univ peop...
68 篇 chinese acad sci...
65 篇 chinese univ hon...
63 篇 institute of inf...
62 篇 google res mount...
61 篇 univ oxford oxfo...
59 篇 univ toronto on
57 篇 swiss fed inst t...

作者

91 篇 van gool luc
87 篇 umapada pal
76 篇 zhang lei
64 篇 lee seong-whan
49 篇 vittorio murino
42 篇 yang yi
34 篇 nassir navab
33 篇 li xin
33 篇 jie yang
32 篇 liu yang
31 篇 escalera sergio
31 篇 loy chen change
30 篇 ling haibin
30 篇 h. bischof
29 篇 zhou jie
29 篇 vasconcelos nuno
29 篇 jan-michael frah...
29 篇 hanqing lu
28 篇 blumenstein mich...
27 篇 jia yunde

语言

51,871 篇 英文
835 篇 其他
241 篇 中文
22 篇 土耳其文
5 篇 西班牙文
2 篇 日文
2 篇 葡萄牙文
2 篇 俄文

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

共 52943 条记录，以下是4691-4700 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：

DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution

DetectoRS: Detecting Objects with Recursive Feature Pyramid ...

引用

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

作者： Qiao, Siyuan Chen, Liang-Chieh Yuille, Alan Johns Hopkins Univ Baltimore MD 21218 USA Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we explore this mechanism in the backbone design for object detection. At the macro level, we propose Recursive Feature Pyramid, which incorporates extra feedback connections from Feature Pyramid Networks into the bottom-up backbone layers. At the micro level, we propose Switchable Atrous Convolution, which convolves the features with different atrous rates and gathers the results using switch functions. Combining them results in DetectoRS, which significantly improves the performances of object detection. On COCO test-dev, DetectoRS achieves state-of-the-art 55.7% box AP for object detection, 48.5% mask AP for instance segmentation, and 50.0% PQ for panoptic segmentation. The code is made publicly available(1).

关键词： computer vision Philosophical considerations Codes Convolution Detectors Switches Object detection

来源：评论

学校读者我要写书评

暂无评论

Application of Environmental Noise Monitoring System Integrated with Visual recognition Technology 5

Application of Environmental Noise Monitoring System Integra...

引用

5th ieee Eurasia conference on IOT, Communication and Engineering, ECICE 2023

作者： Tsao, Yung-Chung Shih, Ming-Chang Kuo, Han-Jung Fang, Jen-Kuang Tsai, Yin-Te National University of Kaohsiung Department of Electrical Engineering Kaohsiung Taiwan Ase Group Corporate R & D Center Kaohsiung Taiwan Providence University Department of Computer Science and Communication Engineering Taichung Taiwan

ISBN: (纸本)9798350314694

Environmental noise is an imperceptible problem in daily life and has a significant impact on human health and quality of life. Thus, noise abnormalities need to be monitored. The cause of the noise is directly related to the environment and human activities. Because of the rapid development of micro-processing machines, image-processing equipment has become easily accessible so visual recognition technology with computer vision and machine learning rapidly develops. Facial recognition is one of the most developed technologies and gradually becomes applicable to various situations such as emotional awareness and safety monitoring. Long-term noise anomaly data and invasion data were collected using cloud edge flatbed integration. The abnormal time data and noise anomaly data were used to build an optimal model of feature detection. © 2023 ieee.

关键词： computer vision decibel environmental noise facial recognition machine learning

来源：评论

学校读者我要写书评

暂无评论

Improving Panoptic Segmentation at All Scales

Improving Panoptic Segmentation at All Scales

引用

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

作者： Porzi, Lorenzo Bulo, Samuel Rota Kontschieder, Peter Facebook Menlo Pk CA 94025 USA

ISBN: (纸本)9781665445092

Crop-based training strategies decouple training resolution from GPU memory consumption, allowing the use of large-capacity panoptic segmentation networks on multi-megapixel images. Using crops, however, can introduce a bias towards truncating or missing large objects. To address this, we propose a novel crop-aware bounding box regression loss (CABS loss), which promotes predictions to be consistent with the visible parts of the cropped objects, while not over-penalizing them for extending outside of the crop. We further introduce a novel data sampling and augmentation strategy which improves generalization across scales by counteracting the imbalanced distribution of object sizes. Combining these two contributions with a carefully designed, top-down panoptic segmentation architecture, we obtain new state-of-the-art results on the challenging Mapillary Vistas (MVD), Indian Driving and Cityscapes datasets, surpassing the previously best approach on MVD by +4.5% PQ and +5.2% mAP.

关键词： Training Measurement Image segmentation computer vision Image resolution Memory management Crops

来源：评论

学校读者我要写书评

暂无评论

Exemplar-Based Open-Set Panoptic Segmentation Network

Exemplar-Based Open-Set Panoptic Segmentation Network

引用

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

作者： Hwang, Jaedong Oh, Seoung Wug Lee, Joon-Young Han, Bohyung Seoul Natl Univ ECE Seoul South Korea Seoul Natl Univ ASRI Seoul South Korea Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

We extend panoptic segmentation to the open-world and introduce an open-set panoptic segmentation (OPS) task. This task requires performing panoptic segmentation for not only known classes but also unknown ones that have not been acknowledged during training. We investigate the practical challenges of the task and construct a benchmark on top of an existing dataset, COCO. In addition, we propose a novel exemplar-based open-set panoptic segmentation network (EOPSN) inspired by exemplar theory. Our approach identifies a new class based on exemplars, which are identified by clustering and employed as pseudoground-truths. The size of each class increases by mining new exemplars based on the similarities to the existing ones associated with the class. We evaluate EOPSN on the proposed benchmark and demonstrate the effectiveness of our proposals. The primary goal of our work is to draw the attention of the community to the recognition in the open-world scenarios. The implementation of our algorithm is available on the project webpage(1).

关键词： Training Image segmentation computer vision Solid modeling Benchmark testing Solids pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Renofeation: A Simple Transfer Learning Method for Improved Adversarial Robustness

Renofeation: A Simple Transfer Learning Method for Improved ...

引用

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

作者： Chin, Ting-Wu Zhang, Cha Marculescu, Diana Carnegie Mellon Univ Pittsburgh PA 15213 USA Microsoft Cloud & AI Redmond WA USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665448994

Fine-tuning through knowledge transfer from a pre-trained model on a large-scale dataset is a widely spread approach to effectively build models on small-scale datasets. In this work, we show that a recent adversarial attack designed for transfer learning via re-training the last linear layer can successfully deceive models trained with transfer learning via end-to-end fine-tuning. This raises security concerns for many industrial applications. In contrast, models trained with random initialization without transfer are much more robust to such attacks, although these models often exhibit much lower accuracy. To this end, we propose noisy feature distillation, a new transfer learning method that trains a network from random initialization while achieving clean-data performance competitive with fine-tuning.

关键词： computer vision Computational modeling conferences Transfer learning Robustness pattern recognition Noise measurement

来源：评论

学校读者我要写书评

暂无评论

Long-Tailed recognition of SAR Aerial View Objects by Cascading and Paralleling Experts

Long-Tailed Recognition of SAR Aerial View Objects by Cascad...

引用

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

作者： Yang, Cheng-Yen Hsu, Hung-Min Cai, Jiarui Hwang, Jenq-Neng Univ Washington Dept Elect & Comp Engn Seattle WA 98195 USA

ISBN: (纸本)9781665448994

Aerial View Object Classification (AVOC) has started to adopt deep learning approaches with significant success in recent years, but limited to optical data. On the other hand, Synthetic Aperture Radar (SAR) has wild aerial view related applications in the remote sensing field. However, SAR has received far less attention due to the special characteristics of the SAR data, which is the long-tailed distribution of the aerial view objects that increases the difficulty of classification. In this paper, we present a two-branch framework, including the cascading expert branch and paralleling expert branch, to tackle the long-tailed distribution of the dataset. Our proposed multi-expert architecture achieves 24.675% and 26.029% in the development phase and testing phase, respectively, in the NTIRE 2021 Multi-modal Aerial View Object Classification Challenge Track I. The proposed method is proved to possess the effectiveness (top-tier performance among 157 participants) and efficiency (i.e., a lightweight architecture) for the AVOC task.

关键词： computer architecture Radar tracking Optical imaging Radar polarimetry pattern recognition Optical sensors Task analysis

来源：评论

学校读者我要写书评

暂无评论

SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos

SoccerNet-v2: A Dataset and Benchmarks for Holistic Understa...

引用

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

作者： Deliege, Adrien Cioppa, Anthony Giancola, Silvio Seikavandi, Meisam J. Dueholm, Jacob, V Nasrollahi, Kamal Ghanem, Bernard Moeslund, Thomas B. Van Droogenbroeck, Marc Univ Liege Liege Belgium KAUST Thuwal Saudi Arabia Aalborg Univ Aalborg Denmark Milestone Syst Brondby Denmark

ISBN: (纸本)9781665448994

Understanding broadcast videos is a challenging task in computer vision, as it requires generic reasoning capabilities to appreciate the content offered by the video editing. In this work, we propose SoccerNet-v2, a novel large-scale corpus of manual annotations for the SoccerNet [24] video dataset, along with open challenges to encourage more research in soccer understanding and broadcast production. Specifically, we release around 300k annotations within SoccerNet's 500 untrimmed broadcast soccer videos. We extend current tasks in the realm of soccer to include action spotting, camera shot segmentation with boundary detection, and we define a novel replay grounding task. For each task, we provide and discuss benchmark results, reproducible with our open-source adapted implementations of the most relevant works in the field. SoccerNet-v2 is presented to the broader research community to help push computer vision closer to automatic solutions for more general video understanding and production purposes.

关键词： computer vision Annotations Grounding Production Benchmark testing Cameras pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking

Probabilistic Tracklet Scoring and Inpainting for Multiple O...

引用

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

作者： Saleh, Fatemeh Aliakbarian, Sadegh Rezatofighi, Hamid Salzmann, Mathieu Gould, Stephen Australian Natl Univ Canberra ACT Australia ACRV Brisbane Qld Australia Monash Univ Melbourne Vic Australia Ecole Polytech Fed Lausanne CVLab Lausanne Switzerland ClearSpace Lausanne Switzerland

ISBN: (纸本)9781665445092

Despite the recent advances in multiple object tracking (MOT), achieved by joint detection and tracking, dealing with long occlusions remains a challenge. This is due to the fact that such techniques tend to ignore the long-term motion information. In this paper, we introduce a probabilistic autoregressive motion model to score tracklet proposals by directly measuring their likelihood. This is achieved by training our model to learn the underlying distribution of natural tracklets. As such, our model allows us not only to assign new detections to existing tracklets, but also to inpaint a tracklet when an object has been lost for a long time, e.g., due to occlusion, by sampling tracklets so as to fill the gap caused by misdetections. Our experiments demonstrate the superiority of our approach at tracking objects in challenging sequences;it outperforms the state of the art in most standard MOT metrics on multiple MOT benchmark datasets, including MOT16, MOT17, and MOT20.

关键词： Training Measurement computer vision Stochastic processes Benchmark testing Probabilistic logic pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Local Binary pattern and Principal Component Analysis for Low-light Face recognition 11

Local Binary Pattern and Principal Component Analysis for Lo...

引用

11th International conference on Electrical Engineering, computer Science and Informatics, EECSI 2024

作者： Masyitoh, Silvia Larasatul Ma'Ruf, Khakam Setiawan, Rizal Justian Yogyakarta State University Dept of Electronics and Informatics Engineering Education Sleman Indonesia Gadjah Mada University Faculty of Engineering Dept of Industrial Engineering Yogyakarta Indonesia College of Engineering Dept of Industrial Engineering and Management Yuan Ze University Zhongli Taiwan

ISBN: (纸本)9798350355314

Facial recognition technology allows both the government and the general public to identify individuals based on their facial features, even in cases of significant changes. However, low lighting during the facial recognition process can lead to confusion during feature extraction, thus affecting facial recognition performance. This research aims to identify the most effective method for recognizing faces in low-light conditions. The Local Binary pattern (LBP) and Principal Component Analysis (PCA) algorithms each have their strengths in feature extraction. These algorithms will be combined with others, including Histogram of Oriented Gradient (HOG), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The process begins with face detection, followed by feature extraction using LBP, PCA, and HOG. Subsequently, these features will be classified using KNN and SVM. The combinations of these algorithms tested in this research include LBP-KNN, LBP-HOG-PCA-SVM, HOG-PCA-SVM, and MobileNet for additional comparison. The results indicate that the HOG-PCA-SVM combination achieved the highest accuracy among the other combinations, reaching 98%. This makes the HOG-PCA-SVM combination a preferred choice for low-light face recognition systems without image enhancement. © 2024 ieee.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Natural Adversarial Examples

Natural Adversarial Examples

引用

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

作者： Hendrycks, Dan Zhao, Kevin Basart, Steven Steinhardt, Jacob Song, Dawn Univ Calif Berkeley Berkeley CA 94720 USA Univ Washington Seattle WA 98195 USA UChicago Chicago IL USA

ISBN: (纸本)9781665445092

We introduce two challenging datasets that reliably cause machine learning model performance to substantially degrade. The datasets are collected with a simple adversarial filtration technique to create datasets with limited spurious cues. Our datasets' real-world, unmodified examples transfer to various unseen models reliably, demonstrating that computer vision models have shared weaknesses. The first dataset is called IMAGENET-A and is like the ImageNet test set, but it is far more challenging for existing models. We also curate an adversarial out-of-distribution detection dataset called IMAGENET-O, which is the first out-of-distribution detection dataset created for ImageNet models. On IMAGENET- A a DenseNet-121 obtains around 2% accuracy, an accuracy drop of approximately 90%, and its out-of-distribution detection performance on IMAGENET- O is near random chance levels. We find that existing data augmentation techniques hardly boost performance, and using other public training datasets provides improvements that are limited. However, we find that improvements to computer vision architectures provide a promising path towards robust models.

关键词： Training computer vision Filtration Convolution Computational modeling Machine learning computer 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 << < 466 467 468 469 470 471 472 473 474 475 > >>

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：