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

6,639 篇 会议
34 篇 期刊文献
5 册 图书

馆藏范围

6,677 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

3,950 篇 工学
- 3,725 篇 计算机科学与技术...
- 1,476 篇 软件工程
- 807 篇 光学工程
- 323 篇 信息与通信工程
- 240 篇 控制科学与工程
- 206 篇 机械工程
- 169 篇 电气工程
- 85 篇 生物医学工程（可授...
- 73 篇 电子科学与技术（可...
- 70 篇 生物工程
- 65 篇 仪器科学与技术
- 38 篇 建筑学
- 36 篇 土木工程
- 34 篇 力学（可授工学、理...
- 32 篇 航空宇航科学与技...
- 29 篇 安全科学与工程
- 23 篇 化学工程与技术
- 21 篇 材料科学与工程（可...
1,498 篇 理学
- 969 篇 物理学
- 929 篇 数学
- 369 篇 统计学（可授理学、...
- 136 篇 生物学
- 40 篇 系统科学
- 26 篇 化学
210 篇 医学
- 210 篇 临床医学
- 23 篇 基础医学(可授医学...
165 篇 管理学
- 123 篇 图书情报与档案管...
- 44 篇 管理科学与工程(可...
- 29 篇 工商管理
21 篇 法学
- 21 篇 社会学
10 篇 农学
9 篇 教育学
6 篇 经济学
2 篇 军事学
1 篇 艺术学

主题

2,364 篇 computer vision
847 篇 pattern recognit...
663 篇 cameras
634 篇 computer science
592 篇 face recognition
555 篇 layout
541 篇 conferences
527 篇 image segmentati...
519 篇 shape
463 篇 object recogniti...
447 篇 robustness
393 篇 humans
340 篇 feature extracti...
320 篇 training
305 篇 object detection
261 篇 image recognitio...
260 篇 application soft...
249 篇 lighting
246 篇 computational mo...
237 篇 image reconstruc...

机构

44 篇 microsoft resear...
27 篇 department of co...
21 篇 swiss fed inst t...
21 篇 school of comput...
21 篇 carnegie mellon ...
20 篇 department of co...
19 篇 swiss fed inst t...
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 center for autom...
14 篇 school of comput...
14 篇 school of comput...
14 篇 computer science...
13 篇 univ maryland co...

作者

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

语言

6,661 篇 英文
9 篇 其他
8 篇 中文

检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

共 6678 条记录，以下是1051-1060 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：

Generalized Class Incremental Learning

Generalized Class Incremental Learning

引用

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

作者： Mi, Fei Kong, Lingjing Lin, Tao Yu, Kaicheng Faltings, Boi Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland

ISBN: (纸本)9781728193601

Many real-world machine learning systems require the ability to continually learn new knowledge. Class incremental learning receives increasing attention recently as a solution towards this goal. However, existing methods often introduce some assumptions to simplify the problem setting, which rarely holds in real-world scenarios. In this paper, we formulate a Generalized Class Incremental Learning (GCIL) framework to systematically alleviate these restrictions, and introduce several novel realistic incremental learning scenarios. In addition, we propose a simple yet effective method, namely ReMix, which combines Exemplar Replay (ER) and Mixup to deal with different challenges in realistic GCIL setups. We demonstrate on CIFAR-100 that ReMix outperforms the state-of-the-art methods in different GCIL setups by significant margins without introducing additional computation cost.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

VoronoiNet General Functional Approximators with Local Support

VoronoiNet General Functional Approximators with Local Suppo...

引用

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

作者： Williams, Francis Parent-Levesque, Jerome Nowrouzezahrai, Derek Panozzo, Daniele Yi, Kwang Moo Tagliasacchi, Andrea NYU New York NY 10003 USA McGill Univ Montreal PQ Canada Univ Victoria Victoria BC Canada Google Brain Mountain View CA USA

ISBN: (纸本)9781728193601

Voronoi diagrams are highly compact representations that are used in various Graphics applications. In this work, we show how to embed a differentiable version of it - via a novel deep architecture - into a generative deep network. By doing so, we achieve a highly compact latent embedding that is able to provide much more detailed reconstructions, both in 2D and 3D, for various shapes. In this tech report, we introduce our representation and present a set of preliminary results comparing it with recently proposed implicit occupancy networks.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

End-to-end Optimized Video Compression with MV-Residual Prediction

End-to-end Optimized Video Compression with MV-Residual Pred...

引用

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

作者： Wu, XiangJi Zhang, Ziwen Feng, Jie Zhou, Lei Wu, Junmin Tucodec Inc Shanghai Peoples R China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

We present an end-to-end trainable framework for P-frame compression in this paper. A joint motion vector (MV) and residual prediction network MV-Residual is designed to extract the ensembled features of motion representations and residual information by treating the two successive frames as inputs. The prior probability of the latent representations is modeled by a hyperprior auto-encoder and trained jointly with the MV-Residual network. Specially, the spatially-displaced convolution is applied for video frame prediction, in which a motion kernel for each pixel is learned to generate predicted pixel by applying the kernel at a displaced location in the source image. Finally, novel rate allocation and post-processing strategies are used to produce the final compressed bits, considering the bits constraint of the challenge. The experimental results on validation set show that the proposed optimized framework can generate the highest MS-SSIM for P-frame compression competition.

关键词： Image coding Video compression Convolution Kernel computer vision conferences pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Class-Balanced Training for Deep Face recognition

Class-Balanced Training for Deep Face Recognition

引用

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

作者： Zhang, Yaobin Deng, Weihong Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781728193601

The performance of deep face recognition depends heavily on the training data. Recently, larger and larger datasets have been developed for the training of deep models. However, most face recognition training sets suffer from the class imbalance problem, and most studies ignore the benefit of optimizing dataset structures. In this paper, we study how class-balanced training can promote face recognition performance. A medium-scale face recognition training set BUPT-CBFace is built by exploring the optimal data structure from massive data. This publicly available dataset is characterized by the uniformly distributed sample size per class, as well as the balance between the number of classes and the number of samples in one class. Experimental results show that deep models trained with BUPT-CBFace can not only achieve comparable results to larger-scale datasets such as MS-Celeb-1M but also alleviate the problem of recognition bias.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Activity-aware Attributes for Zero-Shot Driver Behavior recognition

Activity-aware Attributes for Zero-Shot Driver Behavior Reco...

引用

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

作者： Reiss, Simon Roitberg, Alina Haurilet, Monica Stiefelhagen, Rainer Karlsruhe Inst Technol D-76131 Karlsruhe Germany

ISBN: (纸本)9781728193601

In real-world environments, such as the vehicle cabin, we have to deal with novel concepts as they arise. To this end, we introduce ZS-Drive&Act - the first zero-shot activity classification benchmark specifically aimed at recognizing previously unseen driver behaviors. ZS-Drive&Act is unique due to its focus on fine-grained activities and presence of activity-driven attributes, which are automatically derived from a hierarchical annotation scheme. We adopt and evaluate multiple off-the-shelf zero-shot learning methods on our benchmark, showcasing the difficulties of such models when moving to our application-specific task. We further extend the prominent method based on feature generating Wasserstein GANs with a fusion strategy for linking semantic attributes and word vectors representing the behavior labels. Our experiments demonstrate the effectiveness of leveraging both semantic spaces simultaneously, improving the recognition rate by 2.79%.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification

ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill ...

引用

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

作者： Usuyama, Naoto Delgado, Natalia Larios Hall, Amanda K. Lundin, Jessica Microsoft Healthcare Redmond WA 98052 USA Microsoft Redmond WA USA

ISBN: (纸本)9781728193601

Identifying prescription medications is a frequent task for patients and medical professionals;however, this is an error-prone task as many pills have similar appearances (e.g. white round pills), which increases the risk of medication errors. In this paper, we introduce ePillID, the largest public benchmark on pill image recognition, composed of 13k images representing 8184 appearance classes (two sides for 4092 pill types). For most of the appearance classes, there exists only one reference image, making it a challenging low-shot recognition setting. We present our experimental setup and evaluation results of various baseline models on the benchmark. The best baseline using a multi-head metric-learning approach with bilinear features performed remarkably well;however, our error analysis suggests that they still fail to distinguish particularly confusing classes.

关键词： Image recognition

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Image Classification using Entailment Cone Embeddings

Hierarchical Image Classification using Entailment Cone Embe...

引用

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

作者： Dhall, Ankit Makarova, Anastasia Ganea, Octavian Pavllo, Dario Greeff, Michael Krause, Andreas Swiss Fed Inst Technol Zurich Switzerland MIT Cambridge MA 02139 USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Image classification has been studied extensively, but there has been limited work in using unconventional, external guidance other than traditional image-label pairs for training. We present a set of methods for leveraging information about the semantic hierarchy embedded in class labels. We first inject label-hierarchy knowledge into an arbitrary CNN-based classifier and empirically show that availability of such external semantic information in conjunction with the visual semantics from images boosts overall performance. Taking a step further in this direction, we model more explicitly the label-label and label-image interactions using order-preserving embeddings governed by both Euclidean and hyperbolic geometries, prevalent in natural language, and tailor them to hierarchical image classification and representation learning. We empirically validate all the models on the hierarchical ETHEC dataset.

关键词： Visualization Semantics Task analysis Geometry Computational modeling Apertures computer vision

来源：评论

学校读者我要写书评

暂无评论

CPARR: Category-based Proposal Analysis for Referring Relationships

CPARR: Category-based Proposal Analysis for Referring Relati...

引用

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

作者： He, Chuanzi Zhu, Haidong Gao, Jiyang Chen, Kan Nevatia, Ram Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781728193601

The task of referring relationships is to localize subject and object entities in an image satisfying a relationship query, which is given in the form of . This requires simultaneous localization of the subject and object entities in a specified relationship. We introduce a simple yet effective proposal-based method for referring relationships. Different from the existing methods such as SSAS, our method can generate a high-resolution result while reducing its complexity and ambiguity. Our method is composed of two modules: a category-based proposal generation module to select the proposals related to the entities and a predicate analysis module to score the compatibility of pairs of selected proposals. We show state-of-the-art performance on the referring relationship task on two public datasets: Visual Relationship Detection and Visual Genome.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Leveraging combinatorial testing for safety-critical computer vision datasets

Leveraging combinatorial testing for safety-critical compute...

引用

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

作者： Gladisch, Christoph Heinzemann, Christian Herrmann, Martin Woehrle, Matthias Robert Bosch GmbH Corp Res Gerlingen Germany

ISBN: (纸本)9781728193601

Deep learning-based approaches have gained popularity for environment perception tasks such as semantic segmentation and object detection from images. However, the different nature of a data-driven deep neural nets (DNN) to conventional software is a challenge for practical software verification. In this work, we show how existing methods from software engineering provide benefits for the development of a DNN and in particular for dataset design and analysis. We show how combinatorial testing based on a domain model can be leveraged for generating test sets providing coverage guarantees with respect to important environmental features and their interaction. Additionally, we show how our approach can be used for growing a dataset, i.e. to identify where data is missing and should be collected next. We evaluate our approach on an internal use case and two public datasets.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Few-shot Image recognition for UAV Sports Cinematography

Few-shot Image Recognition for UAV Sports Cinematography

引用

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

作者： Patsiouras, Emmanouil Tefas, Anastasios Pitas, Ioannis Aristotle Univ Thessaloniki Dept Informat Thessaloniki Greece

ISBN: (纸本)9781728193601

The goal of few-shot image learning is to utilize a very small amount of training examples in order to train a machine learning model to recognize a given number of image classes. While humans can perform such a task pretty much effortlessly, applying the same mechanism to deep learning visual recognition systems is a much more difficult task, having a wide range of real-world visual recognition applications. In this paper, we investigate the behavior of such few-shot methods in the context of drone vision cinematography for sports event filming, in order to recognize new image classes by taking into consideration the fact that this new class we wish to identify is a subclass of an already known class. More specifically we use UAV footage to recognize certain types of athletes, belonging to a subset of an original athlete class, utilizing only a handful of recorded images of this athlete subclass. We examine the effects of such methods on image recognition accuracy while proposing a novel approach for accuracy optimizations. The overall task is evaluated on actual cycling race UAV footage.

关键词： few-shot learning image recognition unmaned aerial vehicles

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 << < 102 103 104 105 106 107 108 109 110 111 > >>

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：