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

8,901 篇 会议
43 篇 期刊文献
18 册 图书

馆藏范围

8,961 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

4,560 篇 工学
- 4,020 篇 计算机科学与技术...
- 2,178 篇 软件工程
- 1,241 篇 光学工程
- 555 篇 控制科学与工程
- 431 篇 信息与通信工程
- 430 篇 机械工程
- 294 篇 电气工程
- 287 篇 仪器科学与技术
- 179 篇 生物工程
- 159 篇 生物医学工程（可授...
- 119 篇 电子科学与技术（可...
- 61 篇 安全科学与工程
- 58 篇 建筑学
- 58 篇 化学工程与技术
- 52 篇 土木工程
- 49 篇 交通运输工程
- 40 篇 力学（可授工学、理...
2,065 篇 理学
- 1,382 篇 物理学
- 1,198 篇 数学
- 420 篇 统计学（可授理学、...
- 238 篇 生物学
- 54 篇 化学
- 36 篇 系统科学
263 篇 管理学
- 180 篇 图书情报与档案管...
- 89 篇 管理科学与工程(可...
- 47 篇 工商管理
223 篇 医学
- 222 篇 临床医学
- 39 篇 基础医学(可授医学...
205 篇 艺术学
- 205 篇 设计学（可授艺术学...
45 篇 法学
- 43 篇 社会学
21 篇 农学
14 篇 教育学
9 篇 经济学
6 篇 军事学

主题

3,412 篇 computer vision
1,216 篇 pattern recognit...
946 篇 cameras
908 篇 conferences
765 篇 computer science
674 篇 image segmentati...
618 篇 layout
598 篇 training
548 篇 shape
518 篇 robustness
451 篇 feature extracti...
448 篇 humans
445 篇 face recognition
405 篇 computational mo...
402 篇 object detection
365 篇 visualization
356 篇 computer archite...
336 篇 application soft...
304 篇 lighting
259 篇 image reconstruc...

机构

41 篇 microsoft resear...
30 篇 department of co...
25 篇 department of co...
23 篇 institute for co...
22 篇 department of co...
22 篇 school of comput...
20 篇 university of sc...
20 篇 swiss fed inst t...
19 篇 tsinghua univers...
19 篇 institute of com...
18 篇 swiss fed inst t...
17 篇 the robotics ins...
17 篇 carnegie mellon ...
17 篇 computer vision ...
17 篇 department of co...
16 篇 institute of inf...
16 篇 school of comput...
15 篇 school of comput...
15 篇 carnegie mellon ...
14 篇 national laborat...

作者

57 篇 timofte radu
25 篇 huang thomas s.
24 篇 van gool luc
23 篇 s.k. nayar
22 篇 nayar shree k.
22 篇 t. kanade
21 篇 jain anil k.
20 篇 luc van gool
19 篇 t.s. huang
18 篇 xiaoou tang
18 篇 murino vittorio
18 篇 horst bischof
17 篇 a.k. jain
17 篇 t. darrell
16 篇 g. healey
16 篇 bowyer kevin w.
16 篇 bischof horst
15 篇 m.j. black
15 篇 li stan z.
15 篇 m. shah

语言

8,932 篇 英文
21 篇 其他
8 篇 中文
1 篇 土耳其文

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

共 8962 条记录，以下是391-400 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：

Information Elevation Network for Online Action Detection and Anticipation

Information Elevation Network for Online Action Detection an...

引用

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

作者： Min, Sunah Moon, Jinyoung Elect & Telecommun Res Inst ETRI Daejeon South Korea Univ Sci & Technol UST Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Given a partially observed video segment, online action detection and anticipation aim to identify a current action and forecast future actions, respectively. To detect actions in a streaming video for monitoring applications including surveillance, robot assistants, and autonomous driving, online action detection methods have been proposed. Considering the importance of current action in online action detection, we introduce a novel information elevation unit (IEU) that lifts and accumulates the past information relevant to the current action, to compensate for forgotten essential information. Using the IEUs, we propose an information elevation network (IEN) that effectively identifies a current action and anticipates future actions through the dense prediction of past and current action classes within the video segment. For its practical use in online monitoring applications, our IEN takes visual features extracted from a fast action recognition using only RGB frames because extracting optical flows requires heavy computation overhead. On THUMOS-14 and TVSeries, our IEN outperforms state-of-the-art methods using only RGB frames. Furthermore, on the THUMOS-14 dataset, our IEN outperforms the state-of-the-art methods.

关键词： Visualization computer vision Surveillance conferences Color Streaming media Logic gates

来源：评论

学校读者我要写书评

暂无评论

Nerfels: Renderable Neural Codes for Improved Camera Pose Estimation

Nerfels: Renderable Neural Codes for Improved Camera Pose Es...

引用

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

作者： Avraham, Gil Straub, Julian Shen, Tianwei Yang, Tsun-Yi Germain, Hugo Sweeney, Chris Balntas, Vasileios Novotny, David DeTone, Daniel Newcombe, Richard Monash Univ Clayton Vic Australia Ecole Ponts Champs Sur Marne France Facebook Real Labs Menlo Pk CA USA Facebook AI Res Menlo Pk CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper presents a framework that combines traditional keypoint-based camera pose optimization with an invertible neural rendering mechanism. Our proposed 3D scene representation, Nerfels, is locally dense yet globally sparse. As opposed to existing invertible neural rendering systems which overfit a model to the entire scene, we adopt a feature-driven approach for representing scene-agnostic, local 3D patches with renderable codes. By modelling a scene only where local features are detected, our framework effectively generalizes to unseen local regions in the scene via an optimizable code conditioning mechanism in the neural renderer, all while maintaining the low memory footprint of a sparse 3D map representation. Our model can be incorporated to existing state-of-the-art hand-crafted and learned local feature pose estimators, yielding improved performance when evaluating on ScanNet for wide camera baseline scenarios.

关键词： Solid modeling Three-dimensional displays Codes conferences Pose estimation Rendering (computer graphics) Cameras

来源：评论

学校读者我要写书评

暂无评论

Discriminability-enforcing loss to improve representation learning

Discriminability-enforcing loss to improve representation le...

引用

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

作者： Croitoru, Florinel-Alin Grigore, Diana-Nicoleta Ionescu, Radu Tudor Univ Bucharest Bucharest Romania

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

During the training process, deep neural networks implicitly learn to represent the input data samples through a hierarchy of features, where the size of the hierarchy is determined by the number of layers. In this paper, we focus on enforcing the discriminative power of the high-level representations, that are typically learned by the deeper layers (closer to the output). To this end, we introduce a new loss term inspired by the Gini impurity, which is aimed at minimizing the entropy (increasing the discriminative power) of individual high-level features with respect to the class labels. Although our Gini loss induces highly-discriminative features, it does not ensure that the distribution of the high-level features matches the distribution of the classes. As such, we introduce another loss term to minimize the Kullback-Leibler divergence between the two distributions. We conduct experiments on two image classification data sets (CIFAR-100 and Caltech 101), considering multiple neural architectures ranging from convolutional networks (ResNet-17, ResNet-18, ResNet-50) to transformers (CvT). Our empirical results show that integrating our novel loss terms into the training objective consistently outperforms the models trained with cross-entropy alone, without increasing the inference time at all.

关键词： Training Representation learning Impurities Neural networks computer architecture Transformers Entropy

来源：评论

学校读者我要写书评

暂无评论

CDAD: A Common Daily Action Dataset with Collected Hard Negative Samples

CDAD: A Common Daily Action Dataset with Collected Hard Nega...

引用

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

作者： Xiang, Wangmeng Li, Chao Li, Ke Wang, Biao Hua, Xian-Sheng Zhang, Lei Hong Kong Polytech Univ Hong Kong Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The research on action understanding has achieved significant progress with the establishment of various benchmark datasets. However, the results of action understanding are far from satisfactory in practice. One reason is that the existing action datasets ignore the existence of many hard negative samples in real-world scenarios, which are usually undefined confusion actions, e.g., holding a pen near the mouth vs. smoking. In this work, we focus on the common actions in our daily life and present a novel Common Daily Action Dataset (CDAD), which consists of 57,824 video clips of 23 well-defined common daily actions with rich manual annotations. Particularly, for each daily action, we collect not only diverse positive samples but also various hard negative samples that have minor differences (share similarities) in action with the positive ones. The established CDAD dataset could not only serve as a benchmark for several important daily action understanding tasks, including multi-label action recognition, temporal action localization, and spatial-temporal action detection, but also provide a testbed for researchers to investigate the influence of highly similar negative samples in learning action understanding models.

关键词： Location awareness computer vision Codes conferences Computational modeling Mouth Manuals

来源：评论

学校读者我要写书评

暂无评论

Adversarial Robustness through the Lens of Convolutional Filters

Adversarial Robustness through the Lens of Convolutional Fil...

引用

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

作者： Gavrikov, Paul Keuper, Janis Offenburg Univ IMLA Offenburg Germany Fraunhofer ITWM CC HPC Kaiserslautern Germany Fraunhofer Res Ctr ML Kaiserslautern Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep learning models are intrinsically sensitive to distribution shifts in the input data. In particular, small, barely perceivable perturbations to the input data can force models to make wrong predictions with high confidence. An common defense mechanism is regularization through adversarial training which injects worst-case perturbations back into training to strengthen the decision boundaries, and to reduce overfitting. In this context, we perform an investigation of 3 x 3 convolution filters that form in adversarially-trained models. Filters are extracted from 71 public models of the l(infinity)-RobustBench CIFAR-10/100 and ImageNet1k leaderboard and compared to filters extracted from models built on the same architectures but trained without robust regularization. We observe that adversarially-robust models appear to form more diverse, less sparse, and more orthogonal convolution filters than their normal counterparts. The largest differences between robust and normal models are found in the deepest layers, and the very first convolution layer, which consistently and predominantly forms filters that can partially eliminate perturbations, irrespective of the architecture.

关键词： Training Convolution Perturbation methods computer architecture Predictive models Robustness Data models

来源：评论

学校读者我要写书评

暂无评论

Mitigating Paucity of Data in Sinusoid Characterization Using Generative Synthetic Noise

Mitigating Paucity of Data in Sinusoid Characterization Usin...

引用

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

作者： Sattarzadeh, Sam Shalmani, Shervin Manzuri Azad, Shervin Goldspot Discoveries Corp Montreal PQ Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Although the remarkable breakthrough offered by Deep Learning (DL) models in numerous computer vision tasks, the need to acquire large amounts of high-quality natural data and fine-grained annotations is a shortcoming that fundamentally increases the cost and time devoted to training these models in real-world applications. Hence, synthetic datasets are considered reliable alternatives that can reduce the data acquisition by replacing or merging with natural data or effective pre-training of the models. To this end, in this work, we propose a novel approach to integrate structural data structures with the synthetic noise structures learned by unsupervised models that mimic the noise structures in natural data. Based on the proposed approach, we introduce the Sinusoid Feature recognition (SFR) dataset, which contains hard-to-detect fixed-period sinusoid waves. While the previous works in this regard use generative models to sample synthetic data to inflate the training set, we instead apply unsupervised learning models to generate deep synthetic noise which makes training models in the proposed dataset more challenging. We evaluate the segmentation, image reconstruction, and sinusoid characterization models pre-trained or fully trained on the synthetic SFR dataset on a private dataset of grayscale Acoustic Tele-Viewer (ATV) images. Experimental results show that supervision on our proposed synthetic dataset can improve the accuracy of the models by 3-4% via pre-training, and by 17-27% via ad-hoc training while dealing with challenging, realistic real-world images.

关键词： Training Deep learning Image segmentation computer vision Costs Computational modeling Data acquisition

来源：评论

学校读者我要写书评

暂无评论

MixAugment & Mixup: Augmentation Methods for Facial Expression recognition

MixAugment & Mixup: Augmentation Methods for Facial Expressi...

引用

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

作者： Psaroudakis, Andreas Kollias, Dimitrios Natl Tech Univ Athens Athens Greece Queen Mary Univ London London England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Automatic Facial Expression recognition (FER) has attracted increasing attention in the last 20 years since facial expressions play a central role in human communication. Most FER methodologies utilize Deep Neural Networks (DNNs) that are powerful tools when it comes to data analysis. However, despite their power, these networks are prone to overfitting, as they often tend to memorize the training data. What is more, there are not currently a lot of in-the-wild (i.e. in unconstrained environment) large databases for FER. To alleviate this issue, a number of data augmentation techniques have been proposed. Data augmentation is a way to increase the diversity of available data by applying constrained transformations on the original data. One such technique, which has positively contributed to various classification tasks, is Mixup. According to this, a DNN is trained on convex combinations of pairs of examples and their corresponding labels. In this paper, we examine the effectiveness of Mixup for in-the-wild FER in which data have large variations in head poses, illumination conditions, backgrounds and contexts. We then propose a new data augmentation strategy which is based on Mixup, called MixAugment. According to this, the network is trained concurrently on a combination of virtual examples and real examples;all these examples contribute to the overall loss function. We conduct an extensive experimental study that proves the effectiveness of MixAugment over Mixup and various state-of-the-art methods. We further investigate the combination of dropout with Mixup and MixAugment, as well as the combination of other data augmentation techniques with MixAugment.

关键词： Deep learning computer vision Databases Face recognition conferences Neural networks Training data

来源：评论

学校读者我要写书评

暂无评论

GANDiffFace: Controllable Generation of Synthetic Datasets for Face recognition with Realistic Variations

GANDiffFace: Controllable Generation of Synthetic Datasets f...

引用

ieee/CVF International conference on computer vision (ICCV)

作者： Melzi, Pietro Rathgeb, Christian Tolosana, Ruben Vera-Rodriguez, Ruben Lawatsch, Dominik Domin, Florian Schaubert, Maxim Univ Autonoma Madrid Biometr & Data Pattern Analyt Lab Madrid Spain Secunet Secur Networks AG Essen Germany Hsch Darmstadt Darmstadt Germany

ISBN: (纸本)9798350307443

Face recognition systems have significantly advanced in recent years, driven by the availability of large-scale datasets. However, several issues have recently came up, including privacy concerns that have led to the discontinuation of well-established public datasets. Synthetic datasets have emerged as a solution, even though current synthesis methods present other drawbacks such as limited intraclass variations, lack of realism, and unfair representation of demographic groups. This study introduces GANDiffFace, a novel framework for the generation of synthetic datasets for face recognition that combines the power of Generative Adversarial Networks (GANs) and Diffusion models to overcome the limitations of existing synthetic datasets. In GANDiffFace, we first propose the use of GANs to synthesize highly realistic identities and meet target demographic distributions. Subsequently, we fine-tune Diffusion models with the images generated with GANs, synthesizing multiple images of the same identity with a variety of accessories, poses, expressions, and contexts. We generate multiple synthetic datasets by changing GANDiffFace settings, and compare their mated and non-mated score distributions with the distributions provided by popular real-world datasets for face recognition, i.e. VGG2 and IJB-C. Our results show the feasibility of the proposed GANDiffFace, in particular the use of Diffusion models to enhance the (limited) intra-class variations provided by GANs towards the level of real-world datasets.

关键词： diffusion face recognition generative ai stylegan synthetic

来源：评论

学校读者我要写书评

暂无评论

Efficient Two-stage Model Retraining for Machine Unlearning

Efficient Two-stage Model Retraining for Machine Unlearning

引用

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

作者： Kim, Junyaup Woo, Simon S. PurpleChips Seoul South Korea Sungkyunkwan Univ Seoul South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With the rise of the General Data Protection Regulation (GDPR), user data holders should guarantee the "individual's right to be forgotten". It means user data holders must completely remove user data when they receive the request. However, enabling a deep learning model to exclude specific data used during training is challenging. We cannot easily define the meaning of "forgetting" in deep learning and how to achieve it. To address this issue, we propose an efficient machine unlearning architecture to be used for computer vision classification models. Our approach consists of two-stage models, where in the first stage we enables a deep learning model that loses information with contrastive labels in the requested dataset. Second, we retrain the first stage output model with knowledge distillation (KD). Using this two-stage approach, we can substantiate the removal or forgetness of the requested dataset in the deep learning model. With various datasets used for multimedia applications, we demonstrate that our approach achieves performance on par or even higher accuracy than the original model, while effectively removing the requested data.

关键词： Deep learning Training computer vision Computational modeling conferences computer architecture Data models

来源：评论

学校读者我要写书评

暂无评论

ElasticFace: Elastic Margin Loss for Deep Face recognition

ElasticFace: Elastic Margin Loss for Deep Face Recognition

引用

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

作者： Boutros, Fadi Damer, Naser Kirchbuchner, Florian Kuijper, Arjan Fraunhofer Inst Comp Graph Res IGD D-64283 Darmstadt Germany Tech Univ Darmstadt Dept Comp Sci D-64289 Germany Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Learning discriminative face features plays a major role in building high-performing face recognition models. The recent state-of-the-art face recognition solutions proposed to incorporate a fixed penalty margin on commonly used classification loss function, softmax loss, in the normalized hypersphere to increase the discriminative power of face recognition models, by minimizing the intra-class variation and maximizing the inter-class variation. Marginal penalty softmax losses, such as ArcFace and CosFace, assume that the geodesic distance between and within the different identities can be equally learned using a fixed penalty margin. However, such a learning objective is not realistic for real data with inconsistent inter-and intra-class variation, which might limit the discriminative and generalizability of the face recognition model. In this paper, we relax the fixed penalty margin constrain by proposing elastic penalty margin loss (ElasticFace) that allows flexibility in the push for class separability. The main idea is to utilize random margin values drawn from a normal distribution in each training iteration. This aims at giving the decision boundary chances to extract and retract to allow space for flexible class separability learning. We demonstrate the superiority of our ElasticFace loss over ArcFace and CosFace losses, using the same geometric transformation, on a large set of mainstream benchmarks. From a wider perspective, our ElasticFace has advanced the state-of-the-art face recognition performance on seven out of nine mainstream benchmarks. All training codes, pre-trained models, training logs will be publicly released (1).

关键词： Training computer vision Codes Face recognition conferences Buildings Training data

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 << < 36 37 38 39 40 41 42 43 44 45 > >>

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：