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

6,421 篇 会议
25 篇 期刊文献
3 册 图书

馆藏范围

6,448 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

3,849 篇 工学
- 3,647 篇 计算机科学与技术...
- 1,431 篇 软件工程
- 790 篇 光学工程
- 302 篇 信息与通信工程
- 242 篇 控制科学与工程
- 219 篇 电气工程
- 201 篇 机械工程
- 80 篇 生物医学工程（可授...
- 68 篇 生物工程
- 67 篇 电子科学与技术（可...
- 64 篇 仪器科学与技术
- 36 篇 建筑学
- 33 篇 力学（可授工学、理...
- 33 篇 土木工程
- 33 篇 航空宇航科学与技...
- 26 篇 安全科学与工程
- 22 篇 交通运输工程
- 20 篇 材料科学与工程（可...
- 18 篇 化学工程与技术
1,453 篇 理学
- 945 篇 物理学
- 890 篇 数学
- 352 篇 统计学（可授理学、...
- 134 篇 生物学
- 38 篇 系统科学
- 23 篇 化学
160 篇 管理学
- 110 篇 图书情报与档案管...
- 52 篇 管理科学与工程(可...
- 25 篇 工商管理
112 篇 医学
- 112 篇 临床医学
17 篇 法学
- 17 篇 社会学
12 篇 农学
8 篇 教育学
7 篇 艺术学
6 篇 经济学
2 篇 军事学

主题

2,288 篇 computer vision
789 篇 pattern recognit...
637 篇 cameras
629 篇 computer science
568 篇 face recognition
555 篇 layout
510 篇 image segmentati...
509 篇 conferences
498 篇 shape
445 篇 robustness
439 篇 object recogniti...
388 篇 humans
332 篇 feature extracti...
321 篇 training
303 篇 object detection
262 篇 image recognitio...
257 篇 application soft...
246 篇 lighting
238 篇 image reconstruc...
237 篇 computational mo...

机构

41 篇 microsoft resear...
26 篇 department of co...
21 篇 swiss fed inst t...
21 篇 school of comput...
20 篇 department of co...
19 篇 swiss fed inst t...
19 篇 carnegie mellon ...
18 篇 department of co...
17 篇 department of in...
17 篇 the robotics ins...
17 篇 institute of com...
16 篇 univ sci & techn...
16 篇 robotics institu...
15 篇 tsinghua univ pe...
14 篇 department of el...
14 篇 school of comput...
14 篇 school of comput...
13 篇 univ maryland co...
13 篇 microsoft resear...
13 篇 microsoft resear...

作者

39 篇 timofte radu
28 篇 s.k. nayar
24 篇 huang thomas s.
23 篇 xiaoou tang
22 篇 t. kanade
20 篇 t.s. huang
19 篇 van gool luc
19 篇 t. darrell
19 篇 chellappa rama
18 篇 nayar shree k.
17 篇 a.k. jain
17 篇 a. zisserman
17 篇 jain anil k.
16 篇 g. healey
16 篇 torralba antonio
16 篇 heung-yeung shum
16 篇 zisserman andrew
16 篇 l. van gool
15 篇 m. shah
15 篇 ji qiang

语言

6,447 篇 英文
2 篇 其他

检索条件"任意字段=1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1992"

共 6449 条记录，以下是1111-1120 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：

Font-ProtoNet: Prototypical Network based Font Identification of Document Images in Low Data Regime

Font-ProtoNet: Prototypical Network based Font Identificatio...

引用

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

作者： Goel, Nikita Sharma, Monika Vig, Lovekesh TCS Res New Delhi India

ISBN: (纸本)9781728193601

While optical character recognition has attracted considerable interest from researchers in recent times, automating font identification in printed / scanned documents is still not a well explored problem. With the increasing variety of fonts in the open community, identifying the different fonts used in a given document image can often provide important visual cues for document understanding. Font identification is a challenging task owing to smaller inter-class variations and limited availability of labeled image data for a large variety of font images. In the absence of the original true type format (ttf) files, even synthetic data generation is not possible. To this end, we propose to utilize recent few-shot learning techniques like prototypical networks for font identification in scanned / printed document images using character images from different fonts as input for scarce data scenarios and call the proposed method Font-ProtoNet. This approach uses an initial set of classes to learn an embedding and centroid representations (as class prototypes), that are used to classify novel samples based on euclidean distance. We demonstrate that Font-ProtoNet gives encouraging results by training prototypical networks in few-shot learning settings on a synthetic dataset of 200 font classes and using the trained network to identify fonts on a synthetic dataset of 100 novel font classes. We have also tested our approach on the real-world Adobe Visual Font recognition (AdobeVFR) dataset and obtained 59.86% and 71.01% word-level accuracy of font identification using 1-shot and 5-shot i.e.,1 and 5 character images per font class, respectively.

关键词： Optical character recognition

来源：评论

学校读者我要写书评

暂无评论

Metric Learning with A-based Scalar Product for Image-set recognition

Metric Learning with A-based Scalar Product for Image-set Re...

引用

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

作者： Sogi, Naoya Souza, Lincon S. Gatto, Bernardo B. Fukui, Kazuhiro Univ Tsukuba Grad Sch Sci & Technol Tsukuba Ibaraki Japan Univ Tsukuba Ctr Artificial Intelligence Res C AIR Tsukuba Ibaraki Japan

ISBN: (纸本)9781728193601

In this paper, we propose a metric learning method for image set recognition using subspace representation. The subspace representation is effective for image set recognition where each image set is compactly represented by a subspace in a high dimensional vector space. In this framework, the similarity between two given image sets is measured by the canonical angles between the two corresponding subspaces. Many types of methods utilizing the concept of canonical angles have been developed and studied extensively. However, there still remains large potential in improving the ability to measure canonical angles. Our key idea is to learn a general scalar product space (metric space) that produces more valid canonical angles between two subspaces. To realize this idea, we first introduce an A-based scalar product instead of the standard scalar product, where A is a symmetric positive definite matrix and the canonical angles between two subspaces are measured through the A-based scalar product. We learn a discriminative metric space by optimizing metric A in terms of the Fisher ratio from local Fisher discriminant analysis. Besides, we introduce a mechanism to automatically reduce the dimension of the metric space by imposing a low-rank constraint on metric A. The effectiveness of the proposed methods is validated through extensive classification experiments on three real-world datasets.

关键词： Vector spaces

来源：评论

学校读者我要写书评

暂无评论

Glaucoma Precognition: Recognizing Preclinical Visual Functional Signs of Glaucoma

Glaucoma Precognition: Recognizing Preclinical Visual Functi...

引用

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

作者： Gupta, Krati Thakur, Anshul Goldbaum, Michael Yousefi, Siamak Indian Inst Technol Mandi Sch Comp & Elect Engn Mandi Himachal Prades India Univ Calif San Diego Dept Ophthalmol San Diego CA 92103 USA Univ Tennessee Hlth Sci Ctr Dept Ophthalmol Memphis TN 38103 USA Univ Tennessee Hlth Sci Ctr Dept Genet Genom & Informat Memphis TN USA

ISBN: (纸本)9781728193601

Deep archetypal analysis (DAA) has recently been proposed as an unsupervised approach for discovering latent structures in data. However, while a few approaches have used classical archetypal analysis (AA), DAA has not been incorporated in medical image analysis as yet. The purpose of this study is to develop a precognition framework to identify preclinical signs of glaucomatous vision loss using convex representations derived from DAA. We first develop an AA structure and a novel DAA framework to recognize hidden patterns of visual functional loss, and then project visual field data over the identified patterns to obtain a representation for glaucoma precognition several years prior to disease onset. We then develop a glaucoma classification framework using class-balanced bagging with neural networks to address the class imbalance problem. In contrast to other classification approaches, DAA, applied to a unique prospective longitudinal dataset with approximately eight years of visual field tests from normal eyes that developed glaucoma, has allowed visualization of the early signs of glaucoma and development of a construct for glaucoma precognition. Our findings suggest that our proposed glaucoma precognition approach could significantly advance state-of-the-art glaucoma prediction.

关键词： vision

来源：评论

学校读者我要写书评

暂无评论

LightTrack: A Generic Framework for Online Top-Down Human Pose Tracking

LightTrack: A Generic Framework for Online Top-Down Human Po...

引用

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

作者： Ning, Guanghan Pei, Jian Huang, Heng JD Finance Amer Corp Mountain View CA 94043 USA Simon Fraser Univ Burnaby BC Canada Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (纸本)9781728193601

In this paper, we propose a simple yet effective framework, named LightTrack, for online human pose tracking. Existing methods usually perform human detection, pose estimation and tracking in sequential stages, where pose tracking is regarded as an offline bipartite matching problem. Our proposed framework is designed to be generic, efficient and truly online for top-down approaches. For efficiency, Single-Person Pose Tracking (SPT) and Visual Object Tracking (VOT) are incorporated as a unified online functioning entity, easily implemented by a replaceable single-person pose estimator. To mitigate offline optimization costs, the framework also unifies SPT with online identity association and sheds first light upon bridging multiperson keypoint tracking with Multi-Target Object Tracking (MOT). Specifically, we propose a Siamese Graph Convolution Network (SGCN) for human pose matching as a Re-ID module. In contrary to other Re-ID modules, we use a graphical representation of human joints for matching. The skeleton-based representation effectively captures human pose similarity and is computationally inexpensive. It is robust to sudden camera shifts that introduce human drifting. The proposed framework is general enough to fit other pose estimators and candidate matching mechanisms. Extensive experiments show that our method outperforms other online methods and is very competitive with offline state-of-the-art methods while maintaining higher frame rates. Code and models are publicly available at https://***/Guanghan/lighttrack.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

The 1st Agriculture-vision Challenge: Methods and Results

The 1st Agriculture-Vision Challenge: Methods and Results

引用

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

作者： Chiu, Mang Tik Xu, Xingqian Wang, Kai Hobbs, Jennifer Hovakimyan, Naira Huang, Thomas S. Shi, Honghui Wei, Yunchao Huang, Zilong Schwing, Alexander Brunner, Robert Dozier, Ivan Dozier, Wyatt Ghandilyan, Karen Wilson, David Park, Hyunseong Kim, Junhee Kim, Sungho Liu, Qinghui Kampffmeyer, Michael C. Jenssen, Robert Salberg, Arnt B. Barbosa, Alexandre Trevisan, Rodrigo Zhao, Bingchen Yu, Shaozuo Yang, Siwei Wang, Yin Sheng, Hao Chen, Xiao Su, Jingyi Rajagopal, Ram Ng, Andrew Van Thong Huynh Kim, Soo-Hyung Na, In-Seop Baid, Ujjwal Innani, Shubham Dutande, Prasad Baheti, Bhakti Talbar, Sanjay Tang, Jianyu UIUC Urbana IL 61801 USA Intelinair Indianapolis IN USA Univ Oregon Eugene OR 97403 USA Agcy Def Dev Seoul South Korea DGIST Daegu South Korea Norwegian Comp Ctr Oslo Norway UiT Arctic Univ Norway Oslo Norway Tongji Univ Shanghai Peoples R China Stanford Univ Stanford CA 94305 USA Chegg Inc Santa Clara CA USA Chonnam Natl Univ Gwangju South Korea Chosun Univ Gwangju South Korea SGGS Inst Engn & Technol Nanded India Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

The first Agriculture-vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset. Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation. The Agriculture-vision Challenge Dataset was employed, which comprises of 21,061 aerial and multi-spectral farmland images. This paper provides a summary of notable methods and results in the challenge. Our submission server and leaderboard will continue to open for researchers that are interested in this challenge dataset and task;the link can be found here.

关键词： Semantics Image segmentation pattern recognition conferences Agriculture Computational modeling computer vision

来源：评论

学校读者我要写书评

暂无评论

Semi-Supervised Learning with Scarce Annotations

Semi-Supervised Learning with Scarce Annotations

引用

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

作者： Rebuffi, Sylvestre-Alvise Ehrhardt, Sebastien Han, Kai Vedaldi, Andrea Zisserman, Andrew Univ Oxford Visual Geometry Grp Oxford England

ISBN: (纸本)9781728193601

While semi-supervised learning (SSL) algorithms provide an efficient way to make use of both labelled and unlabelled data, they generally struggle when the number of annotated samples is very small. In this work, we consider the problem of SSL multi-class classification with very few labelled instances. We introduce two key ideas. The first is a simple but effective one: we leverage the power of transfer learning among different tasks and self-supervision to initialize a good representation of the data without making use of any label. The second idea is a new algorithm for SSL that can exploit well such a pre-trained representation. The algorithm works by alternating two phases, one fitting the labelled points and one fitting the unlabelled ones, with carefully-controlled information flow between them. The benefits are greatly reducing overfitting of the labelled data and avoiding issue with balancing labelled and unlabelled losses during training. We show empirically that this method can successfully train competitive models with as few as 10 labelled data points per class. More in general, we show that the idea of bootstrapping features using self-supervised learning always improves SSL on standard benchmarks. We show that our algorithm works increasingly well compared to other methods when refining from other tasks or datasets.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Multiple Transfer Learning and Multi-label Balanced Training Strategies for Facial AU Detection In the Wild

Multiple Transfer Learning and Multi-label Balanced Training...

引用

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

作者： Ji, Sijie Wang, Kai Peng, Xiaojiang Yang, Jianfei Zeng, Zhaoyang Qiao, Yu Nanyang Technol Univ Singapore Singapore Singapore Chinese Acad Sci Shenzhcn Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit SIAT SenseTime Joint Lab Shenzhen Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China

ISBN: (纸本)9781728193601

This paper(1) presents SIAT-NTU solution and results of facial action unit (AU) detection in the EmotiNet Challenge 2020. The task aims to detect 23 AUs from facial images in the wild, and its main difficulties lie in the imbalanced AU distribution and discriminative feature learning. We tackle these difficulties from the following aspects. First, to address the unconstrained heterogeneity of in-the-wild images, we detect and align faces with multi-task convolutional neural networks (MTCNN). Second, by using multiple transfer strategies, we pre-train large CNNs on multiple related datasets, e.g. face recognition datasets and facial expression datasets, and fine-tune them on the EmotiNetdataset. Third, we employ a multi-label balanced sampling strategy and a weighted loss to mitigate the imbalance problem. Last but not the least, to further improve performance, we ensemble multiple models and optimize the thresholds for each AU. Our proposed solution achieves an accuracy of 90.13% and F1 of 44.10% in the final test phase. Our Code is available at: https://***/kaiwang960112/enc2020_au_ detection

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

CatNet: Class Incremental 3D ConvNets for Lifelong Egocentric Gesture recognition

CatNet: Class Incremental 3D ConvNets for Lifelong Egocentri...

引用

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

作者： Wang, Zhengwei She, Qi Chalasani, Tejo Smolic, Aljosa Trinity Coll Dublin V SENSE Dublin Ireland Intel Labs China Beijing Peoples R China

ISBN: (纸本)9781728193601

Egocentric gestures are the most natural form of communication for humans to interact with wearable devices such as VR/AR helmets and glasses. A major issue in such scenarios for real-world applications is that may easily become necessary to add new gestures to the system e.g., a proper VR system should allow users to customize gestures incrementally. Traditional deep learning methods require storing all previous class samples in the system and training the model again from scratch by incorporating previous samples and new samples, which costs humongous memory and significantly increases computation over time. In this work, we demonstrate a lifelong 3D convolutional framework - c(C)la(a)ss increment(t)al net(Net)works (CatNet), which considers temporal information in videos and enables lifelong learning for egocentric gesture video recognition by learning the feature representation of an exemplar set selected from previous class samples. Importantly, we propose a two-stream CatNet, which deploys RGB and depth modalities to train two separate networks. We evaluate CatNets on a publicly available dataset - EgoGesture dataset, and show that CatNets can learn many classes incrementally over a long period of time. Results also demonstrate that the two-stream architecture achieves the best performance on both joint training and class incremental training compared to 3 other one-stream architectures. The codes and pre-trained models used in this work are provided at https://***/villawang/CatNet.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Plastic Surgery: An Obstacle for Deep Face recognition?

Plastic Surgery: An Obstacle for Deep Face Recognition?

引用

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

作者： Rathgeb, Christian Dogan, Didem Stockhardt, Fabian De Marsico, Maria Busch, Christoph Hsch Darmstadt Da Sec Biometr & Internet Secur Res Grp Darmstadt Germany Sapienza Univ Roma Dipartimento Informat Rome Italy

ISBN: (纸本)9781728193601

The impacts of plastic surgery on face recognition systems have been investigated in the past decade by many researchers. Diverse well-known face recognition approaches, e.g. based on PCA or LBP, have been benchmarked mostly on the web-collected IIITD plastic surgery face database. Generally, significant performance drops were reported when comparing facial images taken before and after plastic surgeries. On the one side, some researchers reported problems with said plastic surgery database, i.e. the presence of low quality images. On the other side, the applied methods no longer reflect the state-of-the-art in face recognition. This calls for evaluating the impact of plastic surgery on state-of-the-art deep face recognition systems anew considering high quality imagery of most relevant plastic surgeries. This work introduces the new Hochschule Darmstadt (HDA) plastic surgery database of facial images taken before and after surgery. This database vastly complies with the quality requirements defined by the International Civil Aviation Organization (ICAO) for electronic travel documents and comprises face images of the five most frequently applied facial plastic surgeries. The HDA plastic surgery database, the IIITD plastic surgery database, and a non-surgery database, i.e. ICAO-compliant subsets of the FRGCv2 and FERET datasets, are used for comparative verification and identification evaluations which are conducted using the commercial Cognitec FaceVACS system and the open-source ArcFace system. The obtained results suggest that the impact of plastic surgery on deep face recognition systems is less significant than that observed for previously benchmarked methods.

关键词： Database systems

来源：评论

学校读者我要写书评

暂无评论

Challenges in Recognizing Spontaneous and Intentionally Expressed Reactions to Positive and Negative Images

Challenges in Recognizing Spontaneous and Intentionally Expr...

引用

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

作者： Healey, Jennifer Wang, Haoliang Chhaya, Niyati Adobe Res San Jose CA 95110 USA Adobe Res Bangalore Karnataka India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

This paper presents a preliminary exploration of the challenges of automatically recognizing positive and negative facial expressions in both spontaneous and intentionally expressed conditions. Instead of recognizing iconic basic emotion states, which we have found to be less common in typical human computer interaction, we instead attempted to recognize only positive versus negative states. Our hypothesis was that this would prove more accurate if participants intentionally expressed their feelings. Our study consisted of analyzing video from seven participants, each participating in two sessions. Participants were asked to view 20 images, 10 positive and 10 negative, selected from the OASIS image data set. In the first session participants were instructed to react normally, while in the second session they were asked to intentionally express the emotion they felt when looking at each image. We extracted facial action coding units (AUs) from the recorded video and found that on average, intentionally expressed emotions generated 33% more AU intensity across action units associated with both negative emotions (AU1, AU2, AU4 and AU5) and 117% more intensity for AUs associated with positive emotions (AU6 and AU12). We also show that wide variation exists both in average participant responses across images and in individual reactions to images and that simply taking a ration of our identified action units is not sufficient to determine if a response is positive or negative even in the intentionally expressed case.

关键词： Emotion recognition Lakes Face recognition Encoding Image recognition Gold

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 << < 108 109 110 111 112 113 114 115 116 117 > >>

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：