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4991-5000 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 Gradient Projection Networks for Pan-sharpening

Deep Gradient Projection Networks for Pan-sharpening

引用

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

作者： Xu, Shuang Zhang, Jiangshe Zhao, Zixiang Sun, Kai Liu, Junmin Zhang, Chunxia Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China

ISBN: (纸本)9781665445092

Pan-sharpening is an important technique for remote sensing imaging systems to obtain high resolution multispectral images. Recently, deep learning has become the most popular tool for pan-sharpening. This paper develops a model-based deep pan-sharpening approach. Specifically, two optimization problems regularized by the deep prior are formulated, and they are separately responsible for the generative models for panchromatic images and low resolution multispectral images. Then, the two problems are solved by a gradient projection algorithm, and the iterative steps are generalized into two network blocks. By alternatively stacking the two blocks, a novel network, called gradient projection based pan-sharpening neural network, is constructed. The experimental results on different kinds of satellite datasets demonstrate that the new network outperforms state-of-the-art methods both visually and quantitatively.

关键词： Image resolution Satellites Stacking Neural networks Tools Iterative algorithms pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Continuous Sign Language recognition using Convolutional Neural Network 2

Continuous Sign Language Recognition using Convolutional Neu...

引用

2nd International conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Shankara Narayanan, V. Sneha Varsha, M. Padmavathi, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350328202

Automatic Continuous Sign Language recognition is a task that aims at associating words/phrases for sign language gestures. The scope of building a CSLR framework extends beyond bridging the communication barrier between the deaf community and the hearing majority but includes profound benefits such as bootstrapping translation systems, human-computer interaction, virtual reality environments, etc. In this study, a continuous sign language recognition framework based on a CNN architecture is proposed. Utilizing the Argentina Sign Language dataset, sign language gestures are preprocessed to extract training and testing frames. Employing transfer learning with the ResNet-50 model, a CNN is trained to classify each frame into one of 64 different words present in the dataset. The paper also conducts a thorough post-training data analysis, offering insights into areas where the model performed less effectively, identifying commonly confused pairs of sign language gestures. Additionally, a heatmap overlay technique is employed to elucidate the black-box CNN model. © 2024 ieee.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding

Self-Supervised Learning of Remote Sensing Scene Representat...

引用

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

作者： Stojnic, Vladan Risojevic, Vladimir Univ Banja Luka Fac Elect Engn Banja Luka Bosnia & Herceg

ISBN: (纸本)9781665448994

In recent years self-supervised learning has emerged as a promising candidate for unsupervised representation learning. In the visual domain its applications are mostly studied in the context of images of natural scenes. However, its applicability is especially interesting in specific areas, like remote sensing and medicine, where it is hard to obtain huge amounts of labeled data. In this work, we conduct an extensive analysis of the applicability of self-supervised learning in remote sensing image classification. We analyze the influence of the number and domain of images used for self-supervised pre-training on the performance on downstream tasks. We show that, for the downstream task of remote sensing image classification, using self-supervised pre-training on remote sensing images can give better results than using supervised pre-training on images of natural scenes. Besides, we also show that self-supervised pre-training can be easily extended to multispectral images producing even better results on our downstream tasks.

关键词： Visualization Image segmentation Image coding conferences Object detection Feature extraction pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Regularizing Generative Adversarial Networks under Limited Data

Regularizing Generative Adversarial Networks under Limited D...

引用

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

作者： Tseng, Hung-Yu Jiang, Lu Liu, Ce Yang, Ming-Hsuan Yang, Weilong Google Res Mountain View CA USA Univ Calif Merced Merced CA 95343 USA Waymo Mountain View CA USA Yonsei Univ Seoul South Korea

ISBN: (纸本)9781665445092

Recent years have witnessed the rapid progress of generative adversarial networks (GANs). However, the success of the GAN models hinges on a large amount of training data. This work proposes a regularization approach for training robust GAN models on limited data. We theoretically show a connection between the regularized loss and an f-divergence called LeCam-divergence, which we find is more robust under limited training data. Extensive experiments on several benchmark datasets demonstrate that the proposed regularization scheme 1) improves the generalization performance and stabilizes the learning dynamics of GAN models under limited training data, and 2) complements the recent data augmentation methods. These properties facilitate training GAN models to achieve state-of-the-art performance when only limited training data of the ImageNet benchmark is available. The source code is available at https://***/google/lecam-gan.

关键词： Training computer vision Codes Computational modeling Training data Benchmark testing Fasteners

来源：评论

学校读者我要写书评

暂无评论

AnimalFormer: Multimodal vision Framework for Behavior-based Precision Livestock Farming

AnimalFormer: Multimodal Vision Framework for Behavior-based...

引用

ieee computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Ahmed Qazi Taha Razzaq Asim Iqbal Tibbling Technologies

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce a multimodal vision framework for precision livestock farming, harnessing the power of GroundingDINO, HQSAM, and ViTPose models. This integrated suite enables comprehensive behavioral analytics from video data without invasive animal tagging. GroundingDINO generates accurate bounding boxes around livestock, while HQSAM segments individual animals within these boxes. ViTPose estimates key body points, facilitating posture and movement analysis. Demonstrated on a sheep dataset with grazing, running, sitting, standing, and walking activities, our framework extracts invaluable insights: activity and grazing patterns, interaction dynamics, and detailed postural evaluations. Applicable across species and video resolutions, this framework revolutionizes noninvasive livestock monitoring for activity detection, counting, health assessments, and posture analyses. It empowers data-driven farm management, optimizing animal welfare and productivity through AI-powered behavioral understanding.

关键词： Productivity Legged locomotion Limiting Tagging Livestock Hardware pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Harmonious Semantic Line Detection via Maximal Weight Clique Selection

Harmonious Semantic Line Detection via Maximal Weight Clique...

引用

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

作者： Jin, Dongkwon Park, Wonhui Jeong, Seong-Gyun Kim, Chang-Su Korea Univ Seoul South Korea 42Dot Ai Seoul South Korea

ISBN: (纸本)9781665445092

A novel algorithm to detect an optimal set of semantic lines is proposed in this work. We develop two networks: selection network (S-Net) and harmonization network (H-Net). First, S-Net computes the probabilities and offsets of line candidates. Second, we filter out irrelevant lines through a selection-and-removal process. Third, we construct a complete graph, whose edge weights are computed by H-Net. Finally, we determine a maximal weight clique representing an optimal set of semantic lines. Moreover, to assess the overall harmony of detected lines, we propose a novel metric, called HIoU. Experimental results demonstrate that the proposed algorithm can detect harmonious semantic lines effectively and efficiently. Our codes are available at https://***/dongkwonjin/Semantic-Line-MWCS.

关键词： Measurement computer vision Codes Image edge detection Semantics Detectors Filtering algorithms

来源：评论

学校读者我要写书评

暂无评论

Single-Image Driven 3D Viewpoint Training Data Augmentation for Effective Label recognition 27th

Single-Image Driven 3D Viewpoint Training Data Augmentation ...

引用

27th International conference on pattern recognition, ICPR 2024

作者： Huang, Yueh-Cheng Chen, Hsin-Yi Hung, Cheng-Jui Chuang, Jen-Hui Hwang, Jenq-Neng Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electrical and Computer Engineering University of Washington Seattle United States

ISBN: (纸本)9783031781247

Confronting the critical challenge of insufficient training data in the field of complex image recognition, this paper introduces a novel 3D viewpoint transformation technique initially tailored for label recognition. This technique can be used not only for data augmentation by generating synthetic data from any perspective but also transform photos taken from any angle into a frontal view, thereby reducing the complexity of the recognition task. Given the extensive use of wine-related applications with over 20 million users and the continuous publication of wine label datasets, we decided to focus this study on wine labels. This method enhances deep learning model performance by generating visually realistic training samples from a single real-world label image, overcoming the challenges posed by the intricate combinations of text and logos. Unlike classical Generative Adversarial Network (GAN) methods, which fall short in synthesizing such intricate content combinations and require a large amount of training data to become effective, our proposed solution leverages time-tested computer vision and image processing strategies. By using just a single monocular wine label image, we can expand our training dataset, thereby broadening the range of training samples for deep learning applications. This innovative approach to data augmentation circumvents the constraints of limited training resources. We then utilize the augmented training images through the vision Transformer (ViT) architecture, performing one-shot recognition of existing wine labels in the training classes or future newly collected wine labels unavailable in the training. Experimental results show a significant increase in recognition accuracy over conventional 2D data augmentation techniques, indicating the potential for broader application in various labeling scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Wine

来源：评论

学校读者我要写书评

暂无评论

Isometric Multi-Shape Matching

Isometric Multi-Shape Matching

引用

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

作者： Gao, Maolin Laehner, Zorah Thunberg, Johan Cremers, Daniel Bernard, Florian Tech Univ Munich Munich Germany Halmstad Univ Halmstad Sweden Univ Siegen Siegen Germany

ISBN: (纸本)9781665445092

Finding correspondences between shapes is a fundamental problem in computer vision and graphics, which is relevant for many applications, including 3D reconstruction, object tracking, and style transfer. The vast majority of correspondence methods aim to find a solution between pairs of shapes, even if multiple instances of the same class are available. While isometries are often studied in shape correspondence problems, they have not been considered explicitly in the multi-matching setting. This paper closes this gap by proposing a novel optimisation formulation for isometric multi-shape matching. We present a suitable optimisation algorithm for solving our formulation and provide a convergence and complexity analysis. Our algorithm obtains multi-matchings that are by construction provably cycle-consistent. We demonstrate the superior performance of our method on various datasets and set the new state-of-the-art in isometric multi-shape matching.

关键词： computer vision Three-dimensional displays Shape Linear programming Complexity theory Object tracking Optimization

来源：评论

学校读者我要写书评

暂无评论

TAPA: A Transformer-Driven Framework for Predictive Anomaly Detection via Parallel Learning and pattern-Guided Analysis 6

TAPA: A Transformer-Driven Framework for Predictive Anomaly ...

引用

6th International conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Wang, Yi Zhao, Xin Xi'an Jiaotong University Xi'an China Alibaba Group Holding Limited Beijing China

ISBN: (纸本)9798331541750

Anomaly detection in sequential data has become increasingly critical across various domains, with increasing demands for predictive detection capabilities. While recent advances in deep learning have led to various sophisticated methods, the complex nature of real-world anomalies requires more effective integration of different learning paradigms. This paper introduces a novel Transformer-driven framework that combines parallel learning streams with pattern-guided analysis for predictive anomaly detection. The framework achieves efficient predictive detection through two parallel processes: sequence pattern learning via an improved Transformer architecture and direction-referenced scoring probability distribution learning of sequence behaviors, complemented by pattern-guided analysis with optimized tree structure for pattern recognition. Through extensive experiments on comprehensive sequential datasets, our framework demonstrates superior performance with average precision of 83.13%, recall of 86.24%, and F1-score of 84.54%, consistently outperforming existing methods across different datasets. The results show significant improvements over traditional methods, particularly in capturing complex temporal dependencies and sequential anomaly patterns. © 2024 ieee.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Multimodal Motion Prediction with Stacked Transformers

Multimodal Motion Prediction with Stacked Transformers

引用

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

作者： Liu, Yicheng Zhang, Jinghuai Fang, Liangji Jiang, Qinhong Zhou, Bolei Chinese Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety of autonomous driving. Recent motion prediction approaches attempt to achieve such multimodal motion prediction by implicitly regularizing the feature or explicitly generating multiple candidate proposals. However, it remains challenging since the latent features may concentrate on the most frequent mode of the data while the proposal-based methods depend largely on the prior knowledge to generate and select the proposals. In this work, we propose a novel transformer framework for multimodal motion prediction, termed as mmTransformer. A novel network architecture based on stacked transformers is designed to model the multimodality at feature level with a set of fixed independent proposals. A region-based training strategy is then developed to induce the multimodality of the generated proposals. Experiments on Argoverse dataset show that the proposed model achieves the state-of-the-art performance on motion prediction, substantially improving the diversity and the accuracy of the predicted trajectories. Demo video and code are available at https://***/mmTransformer.

关键词： Training computer vision Computational modeling Predictive models Network architecture Transformers Trajectory

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 << < 491 492 493 494 495 496 497 498 499 500 > >>

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：