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

53,095 篇 会议
1,122 篇 期刊文献
10 册 图书

馆藏范围

54,227 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

26,347 篇 工学
- 17,153 篇 计算机科学与技术...
- 9,460 篇 软件工程
- 8,121 篇 电气工程
- 3,788 篇 控制科学与工程
- 2,426 篇 信息与通信工程
- 2,394 篇 机械工程
- 1,900 篇 生物医学工程（可授...
- 1,313 篇 仪器科学与技术
- 1,054 篇 生物工程
- 778 篇 电子科学与技术（可...
- 756 篇 土木工程
- 716 篇 建筑学
- 680 篇 光学工程
- 531 篇 交通运输工程
- 511 篇 测绘科学与技术
- 390 篇 安全科学与工程
4,525 篇 理学
- 1,749 篇 数学
- 1,317 篇 物理学
- 984 篇 生物学
- 699 篇 系统科学
- 434 篇 统计学（可授理学、...
- 400 篇 化学
3,469 篇 管理学
- 2,120 篇 管理科学与工程(可...
- 1,610 篇 图书情报与档案管...
- 770 篇 工商管理
3,102 篇 医学
- 2,683 篇 临床医学
- 753 篇 基础医学(可授医学...
- 361 篇 药学(可授医学、理...
676 篇 教育学
- 662 篇 教育学
515 篇 农学
- 485 篇 作物学
346 篇 法学
190 篇 经济学
168 篇 艺术学
135 篇 文学
29 篇 军事学
4 篇 历史学
1 篇 哲学

主题

34,750 篇 visualization
7,388 篇 data visualizati...
4,844 篇 training
4,318 篇 feature extracti...
2,961 篇 computational mo...
2,949 篇 three-dimensiona...
2,917 篇 cameras
2,455 篇 computer vision
2,445 篇 semantics
2,073 篇 task analysis
1,940 篇 conferences
1,627 篇 accuracy
1,315 篇 image color anal...
1,302 篇 data mining
1,283 篇 image segmentati...
1,271 篇 navigation
1,203 篇 robustness
1,201 篇 data models
1,156 篇 real-time system...
1,106 篇 measurement

机构

95 篇 university of sc...
94 篇 tsinghua univers...
72 篇 univ chinese aca...
71 篇 shanghai jiao to...
66 篇 zhejiang univers...
59 篇 georgia inst tec...
48 篇 univ konstanz co...
46 篇 georgia institut...
41 篇 facebook ai rese...
41 篇 hong kong univ s...
39 篇 microsoft resear...
37 篇 school of comput...
35 篇 tu wien austria
34 篇 inria
34 篇 univ michigan an...
33 篇 massachusetts in...
32 篇 university of ce...
32 篇 carnegie mellon ...
31 篇 national institu...
30 篇 univ toronto on

作者

53 篇 itoh takayuki
44 篇 zhang lei
31 篇 lei wang
27 篇 jing zhang
26 篇 jun wang
25 篇 yongdong zhang
25 篇 pfister hanspete...
24 篇 schreck tobias
24 篇 van gool luc
24 篇 yang yi
24 篇 li li
22 篇 guangtao zhai
22 篇 nassir navab
22 篇 takahiro ogawa
20 篇 bin wang
20 篇 li xin
19 篇 shuqiang jiang
19 篇 chang remco
19 篇 jie yang
18 篇 yiming li

语言

53,423 篇 英文
563 篇 其他
189 篇 中文
21 篇 西班牙文
14 篇 土耳其文
9 篇 俄文
8 篇 葡萄牙文
1 篇 法文

检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

共 54227 条记录，以下是4081-4090 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：

A Process Reduction Method for Spatial information in Real-Time AR Snow visualization Systems

A Process Reduction Method for Spatial Information in Real-T...

引用

ieee Annual International Computer Software and Applications conference (COMPSAC)

作者： Yasuaki Kobayashi Tomoya Kawakami Satoru Matsumoto Tomoki Yoshihisa Yuuichi Teranishi Graduate School of Engineering University of Fukui Fukui Japan Faculty of Data Science Shiga University Shiga Japan National Institute of Information and Communications Technology Tokyo Japan Cybermedia Center Osaka University Osaka Japan

In this paper, the authors propose a real-time AR visualization system that enables the user to visually grasp the future snow-covered situation at the current location, aiming to support residents and visitors in heavy snow areas. The proposed system generates snow-covered spatial information that reflects the snow-covered situation on spatial information in the real world sensed by 3D LiDAR, and composes it on the video image captured by a camera. This paper describes a lightweight method of spatial information processing by reducing the amount of spatial data transmission.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Addressing out-of-distribution label noise in webly-labelled data 22

Addressing out-of-distribution label noise in webly-labelled...

引用

22nd ieee/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Albert, Paul Ortego, Diego Arazo, Eric O'Connor, Noel E. McGuinness, Kevin Dublin City Univ Insight SFI Ctr Data Analyt Sch Elect Engn Dublin Ireland

ISBN: (纸本)9781665409155

A recurring focus of the deep learning community is towards reducing the labeling effort. Data gathering and annotation using a search engine is a simple alternative to generating a fully human-annotated and human-gathered dataset. Although web crawling is very time efficient, some of the retrieved images are unavoidably noisy, i.e. incorrectly labeled. Designing robust algorithms for training on noisy data gathered from the web is an important research perspective that would render the building of datasets easier. In this paper we conduct a study to understand the type of label noise to expect when building a dataset using a search engine. We review the current limitations of state-of-the-art methods for dealing with noisy labels for image classification tasks in the case of web noise distribution. We propose a simple solution to bridge the gap with a fully clean dataset using Dynamic Softening of Out-of-distribution Samples (DSOS), which we design on corrupted versions of the CIFAR-100 dataset, and compare against state-of-the-art algorithms on the web noise perturbated MiniImageNet and Stanford datasets and on real label noise datasets: WebVision 1.0 and Clothing 1M. Our work is fully reproducible https://***/JKGcj.

关键词： Training visualization Heuristic algorithms Buildings Neural networks Search engines Noise robustness

来源：评论

学校读者我要写书评

暂无评论

Knowledge Capture and Replay for Continual Learning 22

Knowledge Capture and Replay for Continual Learning

引用

22nd ieee/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Gopalakrishnan, Saisubramaniam Singh, Pranshu Ranjan Fayek, Haytham Ramasamy, Savitha Ambikapathi, ArulMurugan ASTAR Inst Infocomm Res I2R Singapore Singapore ASTAR Artificial Intelligence Analyt & Informat AI3 Singapore Singapore RMIT Univ Melbourne Vic Australia CNRS CREATE LTD 1 Create Way08-01 Create Tower Singapore 138602 Singapore

ISBN: (纸本)9781665409155

Deep neural networks model data for a task or a sequence of tasks, where the knowledge extracted from the data is encoded in the parameters and representations of the network. Extraction and utilization of these representations is vital when data is no longer available in the future, especially in a continual learning scenario. We introduce flashcards, which are visual representations that capture the encoded knowledge of a network as a recursive function of some predefined random image patterns. In a continual learning scenario, flashcards help to prevent catastrophic forgetting by consolidating the knowledge of all the previous tasks. Flashcards are required to be constructed only before learning the subsequent task, hence, they are independent of the number of tasks trained before, making them task agnostic. We demonstrate the efficacy of flashcards in capturing learned knowledge representation (as an alternative to the original data), and empirically validate on a variety of continual learning tasks: reconstruction, denoising, and task-incremental classification, using several heterogeneous (varying background and complexity) benchmark datasets. Experimental evidence indicates that: (i) flashcards as a replay strategy is task agnostic, (ii) performs better than generative replay, and (iii) is on par with episodic replay without additional memory overhead.

关键词： Training Deep learning visualization Data privacy Noise reduction Neural networks Knowledge representation

来源：评论

学校读者我要写书评

暂无评论

SAFIT: Segmentation-Aware Scene Flow with Improved Transformer 39

SAFIT: Segmentation-Aware Scene Flow with Improved Transform...

引用

ieee International conference on Robotics and Automation (ICRA)

作者： Shi, Yukang Ma, Kaisheng Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China Tsinghua Univ Inst Interdisciplinary Informat Sci Beijing 100080 Peoples R China

ISBN: (纸本)9781728196817

Scene flow prediction is a challenging task that aims at jointly estimating the 3D structure and 3D motion of dynamic scenes. The previous methods concentrate more on point-wise estimation instead of considering the correspondence between objects as well as lacking the sensation of high-level semantic knowledge. In this paper, we propose a concise yet effective method for scene flow prediction. The key idea is to extend the view of all points for computing point cloud features into object-level, thus simultaneously modeling the relationships of the object-level and point-level via an improved transformer. In addition, we introduce a novel unsupervised loss called segmentation-aware loss, which can model semantic aware details to help predict scene flow more accurately and robustly. Since this loss can be trained without any ground truth, it can be used in both supervised training and selfsupervised training. Experiments on both supervised training and self-supervised training demonstrate the effectiveness of our method. On supervised training, 3.8%, 22.58%, 10.90% and 21.82% accuracy boosts than FLOT [23] can be observed on FT3Ds, KITTIs, FT3Do and KITTIo datasets. On selfsupervised scheme, 48.23% and 48.96% accuracy boost than PointPWC-Net [40] can be observed on KITTIo and KITTIs datasets.

关键词： Training Point cloud compression visualization Three-dimensional displays Computational modeling Semantics Estimation

来源：评论

学校读者我要写书评

暂无评论

Guided Deep Metric Learning

Guided Deep Metric Learning

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Gonzalez-Zapata, Jorge Reyes-Amezcua, Ivan Flores-Araiza, Daniel Mendez-Ruiz, Mauricio Ochoa-Ruiz, Gilberto Mendez-Vazquez, Andres CINVESTAV Unidad Guadalajara Zapopan Mexico Tecnol Monterrey Sch Engn & Sci Monterrey Mexico

ISBN: (纸本)9781665487399

Deep Metric Learning (DML) methods have been proven relevant for visual similarity learning. However, they sometimes lack generalization properties because they are trained often using an inappropriate sample selection strategy or due to the difficulty of the dataset caused by a distributional shift in the data. These represent a significant drawback when attempting to learn the underlying data manifold. Therefore, there is a pressing need to develop better ways of obtaining generalization and representation of the underlying manifold. In this paper, we propose a novel approach to DML that we call Guided Deep Metric Learning, a novel architecture oriented to learning more compact clusters, improving generalization under distributional shifts in DML. This novel architecture consists of two independent models: A multi-branch master model, inspired from a Few-Shot Learning (FSL) perspective, generates a reduced hypothesis space based on prior knowledge from labeled data, which guides or regularizes the decision boundary of a student model during training under an offline knowledge distillation scheme. Experiments have shown that the proposed method is capable of a better manifold generalization and representation to up to 40% improvement (Recall@1, CIFAR10), using guidelines suggested by Musgrave et al. to perform a more fair and realistic comparison, which is currently absent in the literature.

关键词： Measurement Manifolds Training Analytical models Adaptation models visualization Computer architecture

来源：评论

学校读者我要写书评

暂无评论

Semantics-Guided Contrastive Joint Source-Channel Coding for Image Transmission 14

Semantics-Guided Contrastive Joint Source-Channel Coding for...

引用

14th ieee International conference on Wireless Communications and Signal Processing (WCSP)

作者： Hua, Wenhui Chen, Dezhao Fang, Junli Chen, Lingyu Mota, Joao F. C. Hong, Xuemin Xiamen Univ Sch Informat Xiamen Peoples R China Heriot Watt Univ Sch Engn & Phys Sci Edinburgh Scotland

ISBN: (纸本)9781665450850

Deep joint source-channel coding (D-JSCC) provides several advantages over conventional coding schemes, in which source and channel coding are designed separately. For example, D-JSCC schemes suffer from smaller delays and are more robust to rapid channel variation. However, D-JSCCs are often designed without explicit structure or insight, making them less adaptive, hard to control, and theoretically unfounded. In this paper, we propose a contrastive joint-source-channel coding (C-JSCC) design, which uses supervised contrastive learning (SCL) to make the latent space of a conventional D-JSCC more structured and meaningful. By testing on the CIFAR-10 dataset, we show that C-JSCC consistently outperforms its D-JSCC counterpart in both tasks of image reconstruction and classification. Moreover, C-JSCC is shown to output images with perceptual quality better than the classic BPG image codec in the low bits-per-pixel (bpp) region. The roles of various hyper-parameters in C-JSCC are investigated by analytical approximations, experiments, and visualization techniques.

关键词： Contrastive learning joint source-channel coding semantic information image communications

来源：评论

学校读者我要写书评

暂无评论

Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning

Siamese Contrastive Embedding Network for Compositional Zero...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Li, Xiangyu Yang, Xu Wei, Kun Deng, Cheng Yang, Muli Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions formed from seen state and object during training. Since the same state may be various in the visual appearance while entangled with different objects, CZSL is still a challenging task. Some methods recognize state and object with two trained classifiers, ignoring the impact of the interaction between object and state;the other methods try to learn the joint representation of the state-object compositions, leading to the domain gap between seen and unseen composition sets. In this paper, we propose a novel Siamese Contrastive Embedding Network (SCEN)1 for unseen composition recognition. Considering the entanglement between state and object, we embed the visual feature into a Siamese Contrastive Space to capture prototypes of them separately, alleviating the interaction between state and object. In addition, we design a State Transition Module (STM) to increase the diversity of training compositions, improving the robustness of the recognition model. Extensive experiments indicate that our method significantly outperforms the state-of-the-art approaches on three challenging benchmark datasets, including the recent proposed C-QGA dataset.

关键词： Training Deep learning visualization Computer vision Prototypes Training data Robustness

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised Camera Self-Calibration from Video 39

Self-Supervised Camera Self-Calibration from Video

引用

ieee International conference on Robotics and Automation (ICRA)

作者： Fang, Jiading Vasiljevic, Igor Guizilini, Vitor Ambrus, Rares Shakhnarovich, Greg Gaidon, Adrien Walter, Matthew R. Toyota Technol Inst Chicago IL 60637 USA Toyota Res Inst Palo Alto CA USA

ISBN: (纸本)9781728196817

Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams. In practice, calibration is a laborious procedure requiring specialized data collection and careful tuning. This process must be repeated whenever the parameters of the camera change, which can be a frequent occurrence for mobile robots and autonomous vehicles. In contrast, self-supervised depth and ego-motion estimation approaches can bypass explicit calibration by inferring per-frame projection models that optimize a view-synthesis objective. In this paper, we extend this approach to explicitly calibrate a wide range of cameras from raw videos in the wild. We propose a learning algorithm to regress per-sequence calibration parameters using an efficient family of general camera models. Our procedure achieves self-calibration results with sub-pixel reprojection error, outperforming other learning-based methods. We validate our approach on a wide variety of camera geometries, including perspective, fisheye, and catadioptric. Finally, we show that our approach leads to improvements in the downstream task of depth estimation, achieving state-of-the-art results on the EuRoC dataset with greater computational efficiency than contemporary methods. The project page: https://***/ttic. edu/self- sup- self-calib

关键词： visualization Computational modeling Robot vision systems Pose estimation Cameras Calibration Mobile robots

来源：评论

学校读者我要写书评

暂无评论

A Taxonomy of Spatial-Temporal Data visualization 25

A Taxonomy of Spatial-Temporal Data Visualization

引用

25th International conference information Visualisation, IV 2021

作者： Zhu, Ying Kancharla, Pragna Reddy Talluru, Chaitanya Sai Kumar Creative Media Industries Institute Georgia State University Atlanta United States Georgia State University Department of Computer Science Atlanta United States

ISBN: (纸本)9781665438278

Spatial-temporal data visualization is an important sub-field of data visualization. Many spatial-temporal data visualization techniques have been developed and published. Although there are several surveys on this subject, some are based on a task-centric or data-centric framework. Some surveys focus on the temporal data visualization for a particular type of spatial data visualization, such as a map or space-time cube. In this paper, we present a new taxonomy and interactive survey of spatial-temporal data visualization. Based on our taxonomy, a wide variety of spatial-temporal data visualization techniques can be described by a small set of visualization elements. This is the first step towards creating a grammar for spatial-temporal visualization. © 2021 ieee.

关键词： Data visualization

来源：评论

学校读者我要写书评

暂无评论

Towards Open-Set Object Detection and Discovery

Towards Open-Set Object Detection and Discovery

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zheng, Jiyang Li, Weihao Hong, Jie Petersson, Lars Barnes, Nick Australian Natl Univ Canberra ACT Australia Data61 CSIRO Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With the human pursuit of knowledge, open-set object detection (OSOD) has been designed to identify unknown objects in a dynamic world. However, an issue with the current setting is that all the predicted unknown objects share the same category as "unknown", which require incremental learning via a human-in-the-loop approach to label novel classes. In order to address this problem, we present a new task, namely Open-Set Object Detection and Discovery (OSODD). This new task aims to extend the ability of open-set object detectors to further discover the categories of unknown objects based on their visual appearance without human effort. We propose a two-stage method that first uses an open-set object detector to predict both known and unknown objects. Then, we study the representation of predicted objects in an unsupervised manner and discover new categories from the set of unknown objects. With this method, a detector is able to detect objects belonging to known classes and define novel categories for objects of unknown classes with minimal supervision. We show the performance of our model on the MS-COCO dataset under a thorough evaluation protocol. We hope that our work will promote further research towards a more robust real-world detection system.

关键词： visualization Computer vision Protocols conferences Object detection Detectors Human in the loop

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 << < 405 406 407 408 409 410 411 412 413 414 > >>

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：