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

5,349 篇 会议
129 册 图书
29 篇 期刊文献

馆藏范围

5,507 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,748 篇 工学
- 2,253 篇 计算机科学与技术...
- 926 篇 软件工程
- 478 篇 光学工程
- 399 篇 控制科学与工程
- 332 篇 机械工程
- 262 篇 仪器科学与技术
- 250 篇 信息与通信工程
- 128 篇 电气工程
- 122 篇 生物工程
- 90 篇 生物医学工程（可授...
- 70 篇 电子科学与技术（可...
- 39 篇 安全科学与工程
- 33 篇 化学工程与技术
- 31 篇 建筑学
- 27 篇 土木工程
- 25 篇 交通运输工程
- 24 篇 航空宇航科学与技...
859 篇 理学
- 526 篇 物理学
- 439 篇 数学
- 155 篇 统计学（可授理学、...
- 133 篇 生物学
- 28 篇 化学
- 22 篇 系统科学
242 篇 艺术学
- 242 篇 设计学（可授艺术学...
196 篇 管理学
- 101 篇 图书情报与档案管...
- 97 篇 管理科学与工程(可...
- 27 篇 工商管理
96 篇 医学
- 96 篇 临床医学
- 21 篇 基础医学(可授医学...
28 篇 法学
- 26 篇 社会学
8 篇 教育学
6 篇 经济学
5 篇 农学
2 篇 军事学

主题

2,165 篇 computer vision
915 篇 pattern recognit...
890 篇 conferences
640 篇 training
565 篇 cameras
410 篇 feature extracti...
373 篇 computational mo...
364 篇 image segmentati...
350 篇 visualization
338 篇 face recognition
321 篇 computer archite...
309 篇 robustness
290 篇 computer science
287 篇 shape
280 篇 humans
266 篇 object detection
234 篇 layout
196 篇 neural networks
191 篇 application soft...
191 篇 lighting

机构

20 篇 swiss fed inst t...
19 篇 institute for co...
18 篇 university of sc...
18 篇 microsoft resear...
18 篇 swiss fed inst t...
16 篇 carnegie mellon ...
16 篇 chinese acad sci...
14 篇 tsinghua univers...
14 篇 univ sci & techn...
14 篇 carnegie mellon ...
12 篇 harbin inst tech...
12 篇 tsinghua univ pe...
12 篇 department of co...
11 篇 megvii technol p...
11 篇 computer vision ...
11 篇 computer vision ...
10 篇 indian statistic...
10 篇 comp vis ctr bar...
10 篇 school of comput...
10 篇 univ modena & re...

作者

58 篇 timofte radu
17 篇 van gool luc
17 篇 luc van gool
17 篇 horst bischof
16 篇 rita cucchiara
16 篇 radu timofte
14 篇 escalera sergio
14 篇 bischof horst
14 篇 sergio escalera
13 篇 zhigang zhu
13 篇 hugo jair escala...
12 篇 murino vittorio
12 篇 li stan z.
12 篇 chen wei-ting
12 篇 qiang ji
11 篇 cucchiara rita
11 篇 fan haoqiang
11 篇 umapada pal
11 篇 marcos v. conde
11 篇 bowyer kevin w.

语言

5,493 篇 英文
10 篇 中文
5 篇 其他

检索条件"任意字段=2006 Conference on Computer Vision and Pattern Recognition Workshops"

共 5507 条记录，以下是811-820 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：

Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks

Hinge-Wasserstein: Estimating Multimodal Aleatoric Uncertain...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ziliang Xiong Arvi Jonnarth Abdelrahman Eldesokey Joakim Johnander Bastian Wandt Per-Erik Forssén Department of Electrical Engineering Computer Vision Laboratory LiU Sweden Husqvarna Group Huskvarna Sweden Visual Computing Center KAUST Saudi Arabia Zenseact Sweden

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty. We study regression from images to parameter values and here it is common to detect uncertainty by predicting probability distributions. In this context, we investigate the regression-by-classification paradigm which can represent multimodal distributions, without a prior assumption on the number of modes. Through experiments on a specifically designed synthetic dataset, we demonstrate that traditional loss functions lead to poor probability distribution estimates and severe overconfidence, in the absence of full ground truth distributions. In order to alleviate these issues, we propose hinge-Wasserstein – a simple improvement of the Wasserstein loss that reduces the penalty for weak secondary modes during training. This enables prediction of complex distributions with multiple modes, and allows training on datasets where full ground truth distributions are not available. In extensive experiments, we show that the proposed loss leads to substantially better uncertainty estimation on two challenging computer vision tasks: horizon line detection and stereo disparity estimation.

关键词： Training computer vision Uncertainty conferences Estimation Probability distribution pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Essentials for Class Incremental Learning

Essentials for Class Incremental Learning

引用

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

作者： Mittal, Sudhanshu Galesso, Silvio Brox, Thomas Univ Freiburg Freiburg Germany

ISBN: (纸本)9781665448994

Contemporary neural networks are limited in their ability to learn from evolving streams of training data. When trained sequentially on new or evolving tasks, their accuracy drops sharply, making them unsuitable for many real-world applications. In this work, we shed light on the causes of this well known yet unsolved phenomenon often referred to as catastrophic forgetting - in a class-incremental setup. We show that a combination of simple components and a loss that balances intra-task and inter-task learning can already resolve forgetting to the same extent as more complex measures proposed in literature. Moreover, we identify poor quality of the learned representation as another reason for catastrophic forgetting in class-IL. We show that performance is correlated with secondary class information (dark knowledge) learned by the model and it can be improved by an appropriate regularizer. With these lessons learned, class-incremental learning results on CIFAR-100 and ImageNet improve over the state-of-the-art by a large margin, while keeping the approach simple.

关键词： Learning systems computer vision Art conferences Neural networks Training data Boosting

来源：评论

学校读者我要写书评

暂无评论

Differentiable Rendering-based Pose-Conditioned Human Image Generation

Differentiable Rendering-based Pose-Conditioned Human Image ...

引用

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

作者： Horiuchi, Yusuke Simo-Serra, Edgar Iizuka, Satoshi Ishikawa, Hiroshi Waseda Univ Tokyo Japan Univ Tsukuba Tsukuba Ibaraki Japan

ISBN: (纸本)9781665448994

Conditional human image generation, or generation of human images with specified pose based on one or more reference images, is an inherently ill-defined problem, as there can be multiple plausible appearance for parts that are occluded in the reference. Using multiple images can mitigate this problem while boosting the performance. In this work, we introduce a differentiable vertex and edge renderer for incorporating the pose information to realize human image generation conditioned on multiple reference images. The differentiable renderer has parameters that can be jointly optimized with other parts of the system to obtain better results by learning more meaningful shape representation of human pose. We evaluate our method on the Market-1501 and DeepFashion datasets and comparison with existing approaches validates the effectiveness of our approach.

关键词： Heating systems computer vision Image synthesis Shape Image edge detection conferences Training data

来源：评论

学校读者我要写书评

暂无评论

Efficient Skeleton-Based Action recognition for Real-Time Embedded Systems

Efficient Skeleton-Based Action Recognition for Real-Time Em...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Nadhira Noor Fabianaugie Jametoni Jinbeom Kim Hyunsu Hong In Kyu Park Dept. of Electrical and Computer Engineering Inha University Incheon Korea Finedigital Inc Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Action recognition is vital for various real-world applications, yet its implementation on embedded systems or edge devices faces challenges due to limited computing and memory resources. Our goal is to facilitate lightweight action recognition on embedded systems by utilizing skeleton-based techniques, which naturally require less computing and memory resources. To achieve this, we propose innovative methodologies and optimizations tailored for embedded deployment, including post-training quantization, optimized model architectures, and efficient resource utilization. By enabling real-time and lightweight action recognition on resource-constrained embedded systems, our research opens up new possibilities for applications in areas like autonomous surveillance, driving, and indoor safety monitoring.

关键词： Embedded systems Accuracy Quantization (signal) Surveillance Memory management Network architecture Real-time systems

来源：评论

学校读者我要写书评

暂无评论

All You Can Embed: Natural Language based Vehicle Retrieval with Spatio-Temporal Transformers

All You Can Embed: Natural Language based Vehicle Retrieval ...

引用

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

作者： Scribano, Carmelo Sapienza, Davide Franchini, Giorgia Verucchi, Micaela Bertogna, Marko Univ Modena & Reggio Emilia Modena Italy Univ Ferrara Ferrara Italy Univ Parma Parma Italy

ISBN: (纸本)9781665448994

Combining Natural Language with vision represents a unique and interesting challenge in the domain of Artificial Intelligence. The AI City Challenge Track 5 for Natural Language-Based Vehicle Retrieval focuses on the problem of combining visual and textual information, applied to a smart-city use case. In this paper, we present All You Can Embed (AYCE), a modular solution to correlate single-vehicle tracking sequences with natural language. The main building blocks of the proposed architecture are (i) BERT to provide an embedding of the textual descriptions, (ii) a convolutional backbone along with a Transformer model to embed the visual information. For the training of the retrieval model, a variation of the Triplet Margin Loss is proposed to learn a distance measure between the visual and language embeddings. The code is publicly available at https://***/cscribano/AYCE_2021.

关键词： Training Visualization Target tracking Natural languages Urban areas computer architecture Loss measurement

来源：评论

学校读者我要写书评

暂无评论

Data-Efficient Language-Supervised Zero-Shot Learning with Self-Distillation

Data-Efficient Language-Supervised Zero-Shot Learning with S...

引用

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

作者： Cheng, Ruizhe Wu, Bichen Zhang, Peizhao Vajda, Peter Gonzalez, Joseph E. Univ Calif Berkeley Berkeley CA 94720 USA Facebook Real Labs Redmond WA USA

ISBN: (纸本)9781665448994

Traditional computer vision models are trained to predict a fixed set of predefined categories. Recently, natural language has been shown to be a broader and richer source of supervision that provides finer descriptions to visual concepts than supervised "gold" labels. Previous works, such as CLIP, use a simple pretraining task of predicting the pairings between images and text captions. CLIP, however, is data hungry and requires more than 400M image text pairs for training. We propose a data-efficient contrastive distillation method that uses soft labels to learn from noisy image-text pairs. Our model transfers knowledge from pre-trained image and sentence encoders and achieves strong performance with only 3M image text pairs, 133x smaller than CLIP. Our method exceeds the previous SoTA of general zero-shot learning on ImageNet 21k+1k by 73% relatively with a ResNet50 image encoder and DeCLUTR text encoder. We also beat CLIP by 10.5% relatively on zeroshot evaluation on Google Open Images (19,958 classes).

关键词： Training computer vision Visualization Natural languages Predictive models pattern recognition Internet

来源：评论

学校读者我要写书评

暂无评论

Perceptual Image Quality Assessment with Transformers

Perceptual Image Quality Assessment with Transformers

引用

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

作者： Cheon, Manri Yoon, Sung-Jun Kang, Byungyeon Lee, Junwoo LG Elect Seoul South Korea

ISBN: (纸本)9781665448994

In this paper, we propose an image quality transformer (IQT) that successfully applies a transformer architecture to a perceptual full-reference image quality assessment (IQA) task. Perceptual representation becomes more important in image quality assessment. In this context, we extract the perceptual feature representations from each of input images using a convolutional neural network (CNN) backbone. The extracted feature maps are fed into the transformer encoder and decoder in order to compare a reference and distorted images. Following an approach of the transformer-based vision models [18, 55], we use extra learnable quality embedding and position embedding. The output of the transformer is passed to a prediction head in order to predict a final quality score. The experimental results show that our proposed model has an outstanding performance for the standard IQA datasets. For a large-scale IQA dataset containing output images of generative model, our model also shows the promising results. The proposed IQT was ranked first among 13 participants in the NTIRE 2021 perceptual image quality assessment challenge [23]. Our work will be an opportunity to further expand the approach for the perceptual IQA task.

关键词： Image quality Measurement Image resolution Head Feature extraction Quality assessment pattern recognition

来源：评论

学校读者我要写书评

暂无评论

A Strong Baseline for Vehicle Re-Identification

A Strong Baseline for Vehicle Re-Identification

引用

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

作者： Huynh, Su, V Nguyen, Nam H. Nguyen, Ngoc T. Nguyen, Vinh T. Q. Huynh, Chau Chuong Nguyen Cybercore AI Ho Chi Minh City Vietnam

ISBN: (纸本)9781665448994

Vehicle Re-Identification (Re-ID) aims to identify the same vehicle across different cameras, hence plays an important role in modern traffic management systems. The technical challenges require the algorithms must be robust in different views, resolution, occlusion and illumination conditions. In this paper, we first analyze the main factors hindering the Vehicle Re-ID performance. We then present our solutions, specifically targeting the dataset Track 2 of the 5th AI City Challenge, including (1) reducing the domain gap between real and synthetic data, (2) network modification by stacking multi heads with attention mechanism, (3) adaptive loss weight adjustment. Our method achieves 61.34% mAP on the private CityFlow testset without using external dataset or pseudo labeling, and outperforms all previous works at 87.1% mAP on the Veri benchmark. The code is available at https://***/cybercore-co-ltd/track2_aicity_2021.

关键词： Training computer vision Target tracking Head conferences Urban areas Stacking

来源：评论

学校读者我要写书评

暂无评论

CoCon: Cooperative-Contrastive Learning

CoCon: Cooperative-Contrastive Learning

引用

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

作者： Rai, Nishant Adeli, Ehsan Lee, Kuan-Hui Gaidon, Adrien Niebles, Juan Carlos Stanford Univ Stanford CA 94305 USA Toyota Res Inst Toyota Japan

ISBN: (纸本)9781665448994

Labeling videos at scale is impractical. Consequently, self-supervised visual representation learning is key for efficient video analysis. Recent success in learning image representations suggest contrastive learning is a promising framework to tackle this challenge. However, when applied to real-world videos, contrastive learning may unknowingly lead to separation of instances that contain semantically similar events. In our work, we introduce a cooperative variant of contrastive learning to utilize complementary information across views and address this issue. We use data-driven sampling to leverage implicit relationships between multiple input video views, whether observed (e.g. RGB) or inferred (e.g. flow, segmentation masks, poses). We are one of the firsts to explore exploiting inter-instance relationships to drive learning. We experimentally evaluate our representations on the downstream task of action recognition. Our method achieves competitive performance on standard benchmarks (UCF101, HMDB51, Kinetics400). Furthermore, qualitative experiments illustrate that our models can capture higher-order class relationships. The code is available at http://***/nishantrai18/CoCon.

关键词： computer vision Visualization Semantics Performance gain pattern recognition Noise measurement Labeling

来源：评论

学校读者我要写书评

暂无评论

Cross-modal Speaker Verification and recognition: A Multilingual Perspective

Cross-modal Speaker Verification and Recognition: A Multilin...

引用

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

作者： Nawaz, Shah Saeed, Muhammad Saad Morerio, Pietro Mahmood, Arif Gallo, Ignazio Yousaf, Muhammad Haroon Del Bue, Alessio Ist Italiano Tecnol IIT Pattern Anal & Comp Vis PAVIS Genoa Italy Ist Italiano Tecnol IIT Visual Geometry & Modelling VGM Genoa Italy Univ Insubria Varese VA Italy Univ Engn & Technol Taxila Rawalpindi Punjab India Informat Technol Univ Lahore Pakistan

ISBN: (纸本)9781665448994

Recent years have seen a surge in finding association between faces and voices within a cross-modal biometric application along with speaker recognition. Inspired from this, we introduce a challenging task in establishing association between faces and voices across multiple languages spoken by the same set of persons. The aim of this paper is to answer two closely related questions: "Is face-voice association language independent?" and "Can a speaker be recognized irrespective of the spoken language?". These two questions are important to understand effectiveness and to boost development of multilingual biometric systems. To answer these, we collected a Multilingual Audio-Visual dataset, containing human speech clips of 154 identities with 3 language annotations extracted from various videos uploaded online. Extensive experiments on the two splits of the proposed dataset have been performed to investigate and answer these novel research questions that clearly point out the relevance of the multilingual problem.

关键词： Training Annotations Face recognition Pipelines Speech recognition Speaker recognition Task analysis

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 << < 78 79 80 81 82 83 84 85 86 87 > >>

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：