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

6,506 篇 会议
29 篇 期刊文献
10 册 图书

馆藏范围

6,544 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

3,906 篇 工学
- 3,683 篇 计算机科学与技术...
- 1,504 篇 软件工程
- 802 篇 光学工程
- 359 篇 信息与通信工程
- 256 篇 控制科学与工程
- 204 篇 机械工程
- 176 篇 电气工程
- 95 篇 生物医学工程（可授...
- 79 篇 生物工程
- 77 篇 电子科学与技术（可...
- 61 篇 仪器科学与技术
- 38 篇 建筑学
- 36 篇 力学（可授工学、理...
- 34 篇 土木工程
- 31 篇 航空宇航科学与技...
- 31 篇 安全科学与工程
- 22 篇 材料科学与工程（可...
- 22 篇 交通运输工程
1,537 篇 理学
- 980 篇 物理学
- 960 篇 数学
- 376 篇 统计学（可授理学、...
- 152 篇 生物学
- 34 篇 系统科学
- 24 篇 化学
168 篇 管理学
- 120 篇 图书情报与档案管...
- 50 篇 管理科学与工程(可...
- 33 篇 工商管理
139 篇 医学
- 138 篇 临床医学
- 21 篇 基础医学(可授医学...
20 篇 法学
- 19 篇 社会学
11 篇 农学
10 篇 教育学
7 篇 经济学
3 篇 军事学
3 篇 艺术学

主题

2,304 篇 computer vision
871 篇 pattern recognit...
640 篇 cameras
634 篇 computer science
577 篇 face recognition
553 篇 layout
523 篇 image segmentati...
509 篇 conferences
503 篇 shape
450 篇 robustness
438 篇 object recogniti...
390 篇 humans
341 篇 feature extracti...
318 篇 training
307 篇 object detection
261 篇 application soft...
259 篇 image recognitio...
250 篇 lighting
239 篇 image reconstruc...
237 篇 computational mo...

机构

44 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
25 篇 xiaoou tang
25 篇 huang thomas s.
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.
17 篇 zisserman andrew
16 篇 g. healey
16 篇 torralba antonio
16 篇 heung-yeung shum
16 篇 l. van gool
15 篇 zhang lei
15 篇 li stan z.

语言

6,544 篇 英文
1 篇 其他

检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"

共 6545 条记录，以下是361-370 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：

Video Action Detection: Analysing Limitations and Challenges

Video Action Detection: Analysing Limitations and Challenges

引用

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

作者： Modi, Rajat Rana, Aayush Jung Kumar, Akash Tirupattur, Praveen Vyas, Shruti Rawat, Yogesh Singh Shah, Mubarak Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (纸本)9781665487399

Beyond possessing large enough size to feed data hungry machines (eg, transformers), what attributes measure the quality of a dataset? Assuming that the definitions of such attributes do exist, how do we quantify among their relative existences? Our work attempts to explore these questions for video action detection. The task aims to spatio-temporally localize an actor and assign a relevant action class. We first analyze the existing datasets on video action detection and discuss their limitations. Next, we propose a new dataset, Multi Actor Multi Action (MAMA) which overcomes these limitations and is more suitable for real world applications. In addition, we perform a biasness study which analyzes a key property differentiating videos from static images: the temporal aspect. This reveals if the actions in these datasets really need the motion information of an actor, or whether they predict the occurrence of an action even by looking at a single frame. Finally, we investigate the widely held assumptions on the importance of temporal ordering: is temporal ordering important for detecting these actions? Such extreme experiments show existence of biases which have managed to creep into existing methods inspite of careful modeling.

关键词： pattern recognition conferences computer vision

来源：评论

学校读者我要写书评

暂无评论

Unpaired Real-World Super-Resolution with Pseudo Controllable Restoration

Unpaired Real-World Super-Resolution with Pseudo Controllabl...

引用

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

作者： Romero, Andres Van Gool, Luc Timofte, Radu Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Katholieke Univ Leuven Leuven Belgium Univ Wurzburg Wurzburg Germany

ISBN: (纸本)9781665487399

Current super-resolution methods rely on the bicubic down-sampling assumption in order to develop the ill-posed reconstruction of the low-resolution image. Not surprisingly, these approaches fail when using real-world low-resolution images due to the presence of artifacts and intrinsic noise absent in the bicubic setup. Consequently, attention is increasingly paid to techniques that alleviate this problem and super-resolve real-world images. As acquiring paired real-world datasets is a challenging problem, real-world super-resolution solutions are traditionally tackled as a blind problem or as an unpaired data-driven problem. The former makes assumptions about the down-sampling operations, the latter uses unpaired training to learn the real distributions. Recently, blind approaches have dominated this problem by assuming a diverse bank of degradations, whereas the unpaired solutions have shown under-performance due to the two-staged training. In this paper, we propose an unpaired real-world super-resolution method that performs on par, or even better than blind paired approaches by introducing a pseudo-controllable restoration module in a fully end-to-end system.

关键词： Training Degradation computer vision conferences Superresolution Image restoration pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Anomaly Detection from Time-of-Flight Depth Images

Unsupervised Anomaly Detection from Time-of-Flight Depth Ima...

引用

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

作者： Schneider, Pascal Rambach, Jason Mirbach, Bruno Stricker, Didier German Res Ctr Artificial Intelligence DFKI Trippstadter Str 122 D-67663 Kaiserslautern Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Video anomaly detection (VAD) addresses the problem of automatically finding anomalous events in video data. The primary data modalities on which current VAD systems work on are monochrome or RGB images. Using depth data in this context instead is still hardly explored in spite of depth images being a popular choice in many other computer vision research areas and the increasing availability of inexpensive depth camera hardware. We evaluate the application of existing autoencoder-based methods on depth video and propose how the advantages of using depth data can be leveraged by integration into the loss function. Training is done unsupervised using normal sequences without need for any additional annotations. We show that depth allows easy extraction of auxiliary information for scene analysis in the form of a foreground mask and demonstrate its beneficial effect on the anomaly detection performance through evaluation on a large public dataset, for which we are also the first ones to present results on.

关键词： Training Optical losses computer vision Cameras Transformers Sensors Task analysis

来源：评论

学校读者我要写书评

暂无评论

Cross Transferring Activity recognition to Word Level Sign Language Detection

Cross Transferring Activity Recognition to Word Level Sign L...

引用

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

作者： Radhakrishnan, Srijith Mohan, Nikhil C. Varma, Manisimha Varma, Jaithra Pai, Smitha N. Manipal Acad Higher Educ Manipal Inst Technol Dept Informat & Commun Technol Manipal 576104 Karnataka India Manipal Acad Higher Educ Manipal Inst Technol Dept Comp Sci & Engn Manipal 576104 Karnataka India Manipal Acad Higher Educ Manipal Inst Technol Dept Data Sci & Comp Applicat Manipal 576104 Karnataka India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The lack of large scale labelled datasets in word-level sign language recognition (WSLR) poses a challenge to detecting sign language from videos. Most WSLR approaches operate on datasets that do not model real-world settings very well, as they do not have a high degree of variability in terms of signers, background, lighting and inter signer variation. We chose the MS-ASL dataset to overcome these limitations as they model open-world settings very well. This paper benchmarks successful action recognition architectures on the MS-ASL dataset using transfer learning. We have achieved new state-of-the-art accuracy (92.35%) with an improvement of 7.03% over the previous state-of-the-art introduced by the MS-ASL paper. We have analyzed how action-recognition architectures fair in the task of WSLR, and we propose SlowFast 8x8 ResNet 101 as a robust and suitable architecture for the task of WSLR.

关键词： computer vision conferences Transfer learning Lighting Gesture recognition computer architecture Assistive technologies

来源：评论

学校读者我要写书评

暂无评论

Improving Multimodal Speech recognition by Data Augmentation and Speech Representations

Improving Multimodal Speech Recognition by Data Augmentation...

引用

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

作者： Oneata, Dan Cucu, Horia Univ Politehn Bucuresti Speech & Dialogue Res Lab Bucharest Romania

ISBN: (纸本)9781665487399

Multimodal speech recognition aims to improve the performance of automatic speech recognition (ASR) systems by leveraging additional visual information that is usually associated to the audio input. While previous approaches make crucial use of strong visual representations, e.g. by fine-tuning pretrained image recognition networks, significantly less attention has been paid to its counterpart: the speech component. In this work, we investigate ways of improving the base speech recognition system by following similar techniques to the ones used for the visual encoder, namely, transferring representations and data augmentation. First, we show that starting from a pretrained ASR significantly improves the state-of-the-art performance;remarkably, even when building upon a strong unimodal system, we still find gains by including the visual modality. Second, we employ speech data augmentation techniques to encourage the multimodal system to attend to the visual stimuli. This technique replaces previously used word masking and comes with the benefits of being conceptually simpler and yielding consistent improvements in the multimodal setting. We provide empirical results on three multimodal datasets, including the newly introduced Localized Narratives.

关键词： Training Couplings Visualization Image recognition Keyword search Speech recognition Machine learning

来源：评论

学校读者我要写书评

暂无评论

Efficient Conditional Pre-training for Transfer Learning

Efficient Conditional Pre-training for Transfer Learning

引用

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

作者： Chakraborty, Shuvam Uzkent, Burak Ayush, Kumar Tanmay, Kumar Sheehan, Evan Ermon, Stefano Stanford Univ Stanford CA 94305 USA IIT Kharagpur Kharagpur W Bengal India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Almost all the state-of-the-art neural networks for computer vision tasks are trained by (1) pre-training on a large-scale dataset and (2) finetuning on the target dataset. This strategy helps reduce dependence on the target dataset and improves convergence rate and generalization on the target task. Although pre-training on large-scale datasets is very useful for new methods or models, its foremost disadvantage is high training cost. To address this, we propose efficient filtering methods to select relevant subsets from the pre-training dataset. Additionally, we discover that lowering image resolutions in the pre-training step offers a great trade-off between cost and performance. We validate our techniques by pre-training on ImageNet in both the unsupervised and supervised settings and finetuning on a diverse collection of target datasets and tasks. Our proposed methods drastically reduce pre-training cost and provide strong performance boosts. Finally, we improve the current standard of ImageNet pre-training by 1-3% by tuning available models on our subsets and pre-training on a dataset filtered from a larger scale dataset.

关键词： Training computer vision Costs Image resolution Filtering Computational modeling Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Dress Code: High-Resolution Multi-Category Virtual Try-On

Dress Code: High-Resolution Multi-Category Virtual Try-On

引用

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

作者： Morelli, Davide Fincato, Matteo Cornia, Marcella Landi, Federico Cesari, Fabio Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy YOOX NET A PORTER GRP Milan Italy

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Image-based virtual try-on strives to transfer the appearance of a clothing item onto the image of a target person. Existing literature focuses mainly on upper-body clothes (e.g. t-shirts, shirts, and tops) and neglects full-body or lower-body items. This shortcoming arises from a main factor: current publicly available datasets for image-based virtual try-on do not account for this variety, thus limiting progress in the field. In this research activity, we introduce Dress Code, a novel dataset which contains images of multi-category clothes. Dress Code is more than 3x larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images (1024 x 768) with front-view, full-body reference models. To generate HD try-on images with high visual quality and rich in details, we propose to learn fine-grained discriminating features. Specifically, we leverage a semantic-aware discriminator that makes predictions at pixel-level instead of image- or patch-level. The Dress Code dataset is publicly available at https://***/aimagelab/dress-code.

关键词： Visualization computer vision Codes Limiting conferences Clothing pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Towards a Deeper Understanding of Skeleton-based Gait recognition

Towards a Deeper Understanding of Skeleton-based Gait Recogn...

引用

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

作者： Teepe, Torben Gilg, Johannes Herzog, Fabian Hoermann, Stefan Rigoll, Gerhard Tech Univ Munich Munich Germany

ISBN: (纸本)9781665487399

Gait recognition is a promising biometric with unique properties for identifying individuals from a long distance by their walking patterns. In recent years, most gait recognition methods used the person's silhouette to extract the gait features. However, silhouette images can lose fine-grained spatial information, suffer from (self) occlusion, and be challenging to obtain in real-world scenarios. Furthermore, these silhouettes also contain other visual clues that are not actual gait features and can be used for identification, but also to fool the system. Model-based methods do not suffer from these problems and are able to represent the temporal motion of body joints, which are actual gait features. The advances in human pose estimation started a new era for model-based gait recognition with skeleton-based gait recognition. In this work, we propose an approach based on Graph Convolutional Networks (GCNs) that combines higher-order inputs, and residual networks to an efficient architecture for gait recognition. Extensive experiments on the two popular gait datasets, CASIA-B and OUMVLP-Pose, show a massive improvement (3x) of the state-of-the-art (SotA) on the largest gait dataset OUMVLP-Pose and strong temporal modeling capabilities. Finally, we visualize our method to understand skeleton-based gait recognition better and to show that we model real gait features.

关键词： Training Visualization Pose estimation computer architecture Performance gain Feature extraction pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Multimodal Transformer for Nursing Activity recognition

Multimodal Transformer for Nursing Activity Recognition

引用

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

作者： Ijaz, Momal Diaz, Renato Chen, Chen Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In an aging population, elderly patient safety is a primary concern at hospitals and nursing homes, which demands for increased nurse care. By performing nurse activity recognition, we can not only make sure that all patients get an equal desired care, but it can also free nurses from manual documentation of activities they perform, leading to a fair and safe place of care for the elderly. In this work, we present a multimodal transformer-based network, which extracts features from skeletal joints and acceleration data, and fuses them to perform nurse activity recognition. Our method achieves state-of-the-art performance of 81.8% accuracy on the benchmark dataset available for nurse activity recognition from the Nurse Care Activity recognition Challenge. We perform ablation studies to show that our fusion model is better than single modality transformer variants (using only acceleration or skeleton joints data). Our solution also outperforms state-of-the-art ST-GCN, GRU and other classical hand-crafted-feature-based classifier solutions by a margin of 1.6%, on the NCRC dataset. Code is available at https://***/Momilijaz96/MMT_for_NCRC.

关键词： Hospitals Sociology Manuals Activity recognition Transformers Feature extraction Skeleton

来源：评论

学校读者我要写书评

暂无评论

PseudoProp: Robust Pseudo-Label Generation for Semi-Supervised Object Detection in Autonomous Driving Systems

PseudoProp: Robust Pseudo-Label Generation for Semi-Supervis...

引用

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

作者： Hu, Shu Liu, Chun-Hao Dutta, Jayanta Chang, Ming-Ching Lyu, Siwei Ramakrishnan, Naveen Univ Buffalo SUNY Buffalo NY USA Bosch Ctr Artificial Intelligence Sunnyvale CA 94085 USA SUNY Albany Albany NY 12222 USA Amazon Seattle WA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Semi-supervised object detection methods are widely used in autonomous driving systems, where only a fraction of objects are labeled. To propagate information from the labeled objects to the unlabeled ones, pseudo-labels for unlabeled objects must be generated. Although pseudo-labels have proven to improve the performance of semisupervised object detection significantly, the applications of image-based methods to video frames result in numerous miss or false detections using such generated pseudo-labels. In this paper, we propose a new approach, PseudoProp, to generate robust pseudo-labels by leveraging motion continuity in video frames. Specifically, PseudoProp uses a novel bidirectional pseudo-label propagation approach to compensate for misdetection. A feature-based fusion technique is also used to suppress inference noise. Extensive experiments on the large-scale Cityscapes dataset demonstrate that our method outperforms the state-of-the-art semi-supervised object detection methods by 7.4% on mAP(75).

关键词： Training computer vision conferences Object detection Predictive models Feature extraction pattern recognition

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 << < 33 34 35 36 37 38 39 40 41 42 > >>

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：