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

12,844 篇 会议
13 篇 期刊文献
2 册 图书

馆藏范围

12,859 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

7,573 篇 工学
- 6,863 篇 计算机科学与技术...
- 880 篇 机械工程
- 814 篇 软件工程
- 435 篇 控制科学与工程
- 360 篇 光学工程
- 306 篇 电气工程
- 209 篇 仪器科学与技术
- 124 篇 信息与通信工程
- 91 篇 生物工程
- 62 篇 生物医学工程（可授...
- 39 篇 电子科学与技术（可...
- 34 篇 安全科学与工程
- 26 篇 化学工程与技术
- 21 篇 交通运输工程
- 20 篇 建筑学
- 18 篇 土木工程
2,957 篇 医学
- 2,956 篇 临床医学
- 15 篇 基础医学(可授医学...
- 12 篇 药学(可授医学、理...
700 篇 理学
- 359 篇 物理学
- 225 篇 数学
- 175 篇 系统科学
- 95 篇 统计学（可授理学、...
- 93 篇 生物学
- 22 篇 化学
201 篇 艺术学
- 201 篇 设计学（可授艺术学...
84 篇 管理学
- 59 篇 图书情报与档案管...
- 25 篇 管理科学与工程(可...
- 14 篇 工商管理
23 篇 法学
- 21 篇 社会学
5 篇 农学
4 篇 教育学
2 篇 经济学
1 篇 军事学

主题

6,464 篇 computer vision
2,688 篇 training
2,437 篇 pattern recognit...
1,780 篇 computational mo...
1,522 篇 visualization
1,348 篇 three-dimensiona...
1,091 篇 computer archite...
1,063 篇 semantics
997 篇 benchmark testin...
976 篇 codes
970 篇 conferences
854 篇 feature extracti...
830 篇 cameras
771 篇 task analysis
707 篇 deep learning
646 篇 image segmentati...
611 篇 object detection
595 篇 shape
554 篇 transformers
538 篇 neural networks

机构

132 篇 univ sci & techn...
122 篇 carnegie mellon ...
120 篇 tsinghua univ pe...
114 篇 univ chinese aca...
113 篇 chinese univ hon...
94 篇 tsinghua univers...
91 篇 zhejiang univ pe...
91 篇 swiss fed inst t...
85 篇 peng cheng lab p...
81 篇 university of ch...
80 篇 zhejiang univers...
77 篇 shanghai ai lab ...
77 篇 peng cheng labor...
75 篇 university of sc...
69 篇 shanghai jiao to...
68 篇 shanghai jiao to...
67 篇 alibaba grp peop...
67 篇 stanford univ st...
66 篇 univ hong kong p...
64 篇 sensetime res pe...

作者

77 篇 timofte radu
63 篇 van gool luc
45 篇 zhang lei
36 篇 yang yi
36 篇 luc van gool
34 篇 tao dacheng
31 篇 loy chen change
29 篇 chen chen
28 篇 sun jian
28 篇 qi tian
25 篇 li xin
24 篇 liu yang
24 篇 tian qi
24 篇 ying shan
23 篇 wang xinchao
23 篇 zha zheng-jun
23 篇 boxin shi
21 篇 zhou jie
21 篇 vasconcelos nuno
20 篇 luo ping

语言

12,851 篇 英文
7 篇 其他
1 篇 中文

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

共 12859 条记录，以下是4471-4480 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：

Learned Initializations for Optimizing Coordinate-Based Neural Representations

Learned Initializations for Optimizing Coordinate-Based Neur...

引用

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

作者： Tancik, Matthew Mildenhall, Ben Wang, Terrance Schmidt, Divi Srinivasan, Pratul P. Barron, Jonathan T. Ng, Ren Univ Calif Berkeley Berkeley CA 94720 USA Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

Coordinate-based neural representations have shown significant promise as an alternative to discrete, array-based representations for complex low dimensional signals. However, optimizing a coordinate-based network from randomly initialized weights for each new signal is inefficient. We propose applying standard meta-learning algorithms to learn the initial weight parameters for these fully-connected networks based on the underlying class of signals being represented (e.g., images of faces or 3D models of chairs). Despite requiring only a minor change in implementation, using these learned initial weights enables faster convergence during optimization and can serve as a strong prior over the signal class being modeled, resulting in better generalization when only partial observations of a given signal are available. We explore these benefits across a variety of tasks, including representing 2D images, reconstructing CT scans, and recovering 3D shapes and scenes from 2D image observations.

关键词： Solid modeling Three-dimensional displays Limiting Shape Trajectory pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences

HumanGPS: Geodesic PreServing Feature for Dense Human Corres...

引用

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

作者： Tan, Feitong Tang, Danhang Dou, Mingsong Guo, Kaiwen Pandey, Rohit Keskin, Cem Du, Ruofei Sun, Deqing Bouaziz, Sofien Fanello, Sean Tan, Ping Zhang, Yinda Google Mountain View CA 94043 USA Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665445092

In this paper, we address the problem of building dense correspondences between human images under arbitrary camera viewpoints and body poses. Prior art either assumes small motion between frames or relies on local descriptors, which cannot handle large motion or visually ambiguous body parts, e.g., left vs. right hand. In contrast, we propose a deep learning framework that maps each pixel to a feature space, where the feature distances reflect the geodesic distances among pixels as if they were projected onto the surface of a 3D human scan. To this end, we introduce novel loss functions to push features apart according to their geodesic distances on the surface. Without any semantic annotation, the proposed embeddings automatically learn to differentiate visually similar parts and align different subjects into an unified feature space. Extensive experiments show that the learned embeddings can produce accurate correspondences between images with remarkable generalization capabilities on both intra and inter subjects.(1)

关键词： Deep learning Image segmentation computer vision Three-dimensional displays Shape Semantics Buildings

来源：评论

学校读者我要写书评

暂无评论

SurFree: a fast surrogate-free black-box attack

SurFree: a fast surrogate-free black-box attack

引用

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

作者： Maho, Thibault Furon, Teddy Le Merrer, Erwan Univ Rennes CNRS INRIA IRISA Rennes France

ISBN: (纸本)9781665445092

Machine learning classifiers are critically prone to evasion attacks. Adversarial examples are slightly modified inputs that are then misclassified, while remaining perceptively close to their originals. Last couple of years have witnessed a striking decrease in the amount of queries a black box attack submits to the target classifier, in order to forge adversarials. This particularly concerns the black box score-based setup, where the attacker has access to top predicted probabilites: the amount of queries went from to millions of to less than a thousand. This paper presents SurFree, a geometrical approach that achieves a drastic reduction in the amount of queries in the hardest setup: black box decision-based attacks (only the top-1 label is available). We first highlight that the most recent attacks in that setup, HSJA [3], QEBA [14] and GeoDA [23] all perform costly gradient surrogate estimations. SurFree proposes to bypass these, by instead focusing on careful trials along diverse directions, guided by precise indications of geometrical properties of the classifier decision boundaries. We motivate this geometric approach before performing a head-to-head comparison with previous attacks with the amount of queries as a first class citizen. We exhibit a faster distortion decay under low query amounts (few hundreds to a thousand), while remaining competitive at higher query budgets.(1)

关键词： computer vision Estimation Focusing Machine learning Distortion pattern recognition Convergence

来源：评论

学校读者我要写书评

暂无评论

Cascaded Prediction Network via Segment Tree for Temporal Video Grounding

Cascaded Prediction Network via Segment Tree for Temporal Vi...

引用

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

作者： Zhao, Yang Zhao, Zhou Zhang, Zhu Lin, Zhijie Zhejiang Univ Coll Comp Sci Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

Temporal video grounding aims to localize the target segment which is semantically aligned with the given sentence in an untrimmed video. Existing methods can be divided into two main categories, including proposal-based approaches and proposal-free approaches. However, the former ones suffer from the extra cost of generating proposals and inflexibility in determining fine-grained boundaries, and the latter ones usually attempt to decide the start and end timestamps directly, which brings about much difficulty and inaccuracy. In this paper, we convert this task into a multi-step decision problem and propose a novel Cascaded Prediction Network (CPN) to generate the grounding result in a coarse-to-fine manner. Concretely, we first encode video and query into the same latent space and fuse them into integrated representations. Afterwards, we construct a segment-tree-based structure and make predictions via decision navigation and signal decomposition in a cascaded way. We evaluate our proposed method on three large-scale publicly available benchmarks, namely ActivityNet Caption, Charades-STA and TACoS, where our CPN surpasses the performance of the state-of-the-art methods.

关键词： computer vision Costs Grounding Navigation Fuses Benchmark testing pattern recognition

来源：评论

学校读者我要写书评

暂无评论

T2FNorm: Train-time Feature Normalization for OOD Detection in Image Classification

T2FNorm: Train-time Feature Normalization for OOD Detection ...

引用

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

作者： Sudarshan Regmi Bibek Panthi Sakar Dotel Prashnna K Gyawali Danail Stoyanov Binod Bhattarai University College London UK NepAl Applied Mathematics and Informatics Institute for Research Nepal Tribhuvan University Nepal West Virginia University USA University of Aberdeen UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural networks are notorious for being overconfident predictors, posing a significant challenge to their safe deployment in real-world applications. While feature normalization has garnered considerable attention within the deep learning literature, current train-time regularization methods for Out-of-Distribution(OOD) detection are yet to fully exploit this potential. Indeed, the naive incorporation of feature normalization within neural networks does not guarantee substantial improvement in OOD detection performance. In this work, we introduce T2FNorm, a novel approach to transforming features to hyperspherical space during training, while employing non-transformed space for OOD-scoring purposes. This method yields a surprising enhancement in OOD detection capabilities without compromising model accuracy in in-distribution(ID). Our investigation demonstrates that the proposed technique substantially diminishes the norm of the features of all samples, more so in the case of out-of-distribution samples, thereby addressing the prevalent concern of overconfidence in neural networks. The proposed method also significantly improves various post-hoc OOD detection methods.

关键词： Training Measurement Deep learning computer vision Accuracy conferences Neural networks

来源：评论

学校读者我要写书评

暂无评论

Gradient-based Algorithms for Machine Teaching

Gradient-based Algorithms for Machine Teaching

引用

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

作者： Wang, Pei Nagrecha, Kabir Vasconcelos, Nuno Univ Calif San Diego San Diego CA 92093 USA

ISBN: (纸本)9781665445092

The problem of machine teaching is considered. A new formulation is proposed under the assumption of an optimal student, where optimality is defined in the usual machine learning sense of empirical risk minimization. This is a sensible assumption for machine learning students and for human students in crowdsourcing platforms, who tend to perform at least as well as machine learning systems. It is shown that, if allowed unbounded effort, the optimal student always learns the optimal predictor for a classification task. Hence, the role of the optimal teacher is to select the teaching set that minimizes student effort. This is formulated as a problem of functional optimization where, at each teaching iteration, the teacher seeks to align the steepest descent directions of the risk of (1) the teaching set and (2) entire example population. The optimal teacher, denoted MaxGrad, is then shown to maximize the gradient of the risk on the set of new examples selected per iteration. MaxGrad teaching algorithms are finally provided for both binary and multiclass tasks, and shown to have some similarities with boosting algorithms. Experimental evaluations demonstrate the effectiveness of MaxGrad, which outperforms previous algorithms on the classification task, for both machine learning and human students from MTurk, by a substantial margin.

关键词： Training Machine learning algorithms Education Sociology Classification algorithms pattern recognition Risk management

来源：评论

学校读者我要写书评

暂无评论

Tackling Domain Shifts in Person Re-Identification: A Survey and Analysis

Tackling Domain Shifts in Person Re-Identification: A Survey...

引用

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

作者： Vuong D. Nguyen Samiha Mirza Abdollah Zakeri Ayush Gupta Khadija Khaldi Rahma Aloui Pranav Mantini Shishir K. Shah Fatima Merchant University of Houston Johns Hopkins University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The necessity for a Person ReID system for rapidly evolving urban surveillance applications is severely challenged by domain shifts—variations in data distribution that occur across different environments or times. In this paper, we provide the first empirical review of domain shift in person ReID, which includes three settings namely Unsupervised Domain Adaptation ReID, Domain Generalizable ReID, and Lifelong ReID. We observe that existing approaches only tackle domain shifts caused by cross-dataset setting, while ignoring intra-dataset attribute domain shifts caused by changes in clothing, shape, or gait, which is very common in ReID. Thus, we enhance research directions in this field by redefining domain shift in ReID as the combination of attribute domain shift with cross-dataset domain shift. With a focus on Lifelong Re-ID methods, we conduct an extensive comparison on a fair experimental setup and provide an in-depth analysis of these methods under both non-cloth-changing and cloth-changing Re-ID scenarios. Insights into the strengths and limitations of these methods based on their performance are studied. This paper outlines future research directions and paves the way for the development of more adaptive, resilient, and enduring cross-domain ReID systems. Code is available here.

关键词： Surveys computer vision Codes Shape Reviews Surveillance conferences

来源：评论

学校读者我要写书评

暂无评论

Enhancing the Transferability of Adversarial Attacks through Variance Tuning

Enhancing the Transferability of Adversarial Attacks through...

引用

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

作者： Wang, Xiaosen He, Kun Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9781665445092

Deep neural networks are vulnerable to adversarial examples that mislead the models with imperceptible perturbations. Though adversarial attacks have achieved incredible success rates in the white-box setting, most existing adversaries often exhibit weak transferability in the black-box setting, especially under the scenario of attacking models with defense mechanisms. In this work, we propose a new method called variance tuning to enhance the class of iterative gradient based attack methods and improve their attack transferability. Specifically, at each iteration for the gradient calculation, instead of directly using the current gradient for the momentum accumulation, we further consider the gradient variance of the previous iteration to tune the current gradient so as to stabilize the update direction and escape from poor local optima. Empirical results on the standard ImageNet dataset demonstrate that our method could significantly improve the transferability of gradient-based adversarial attacks. Besides, our method could be used to attack ensemble models or be integrated with various input transformations. Incorporating variance tuning with input transformations on iterative gradient-based attacks in the multi-model setting, the integrated method could achieve an average success rate of 90.1% against nine advanced defense methods, improving the current best attack performance significantly by 85.1%.

关键词： Deep learning computer vision Codes Perturbation methods Computational modeling pattern recognition Iterative methods

来源：评论

学校读者我要写书评

暂无评论

Image Change Captioning by Learning from an Auxiliary Task

Image Change Captioning by Learning from an Auxiliary Task

引用

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

作者： Hosseinzadeh, Mehrdad Wang, Yang Univ Manitoba Winnipeg MB Canada Huawei Technol Canada Markham ON Canada

ISBN: (纸本)9781665445092

We tackle the challenging task of image change captioning. The goal is to describe the subtle difference between two very similar images by generating a sentence caption. While the recent methods mainly focus on proposing new model architectures for this problem, we instead focus on an alternative training scheme. Inspired by the success of multi-task learning, we formulate a training scheme that uses an auxiliary task to improve the training of the change captioning network. We argue that the task of composed query image retrieval is a natural choice as the auxiliary task. Given two almost similar images as the input, the primary network generates a caption describing the fine change between those two images. Next, the auxiliary network is provided with the generated caption and one of those two images. It then tries to pick the second image among a set of candidates. This forces the primary network to generate detailed and precise captions via having an extra supervision loss by the auxiliary network. Furthermore, we propose a new scheme for selecting a negative set of candidates for the retrieval task that can effectively improve the performance. We show that the proposed training strategy performs well on the task of change captioning on benchmark datasets.

关键词： Training computer vision Image color analysis Image retrieval Semantics Benchmark testing pattern recognition

来源：评论

学校读者我要写书评

暂无评论

CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning

CReST: A Class-Rebalancing Self-Training Framework for Imbal...

引用

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

作者： Wei, Chen Sohn, Kihyuk Mellina, Clayton Yuille, Alan Yang, Fan Johns Hopkins Univ Baltimore MD 21218 USA Google Cloud AI Mountain View CA USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Semi-supervised learning on class-imbalanced data, although a realistic problem, has been under studied. While existing semi-supervised learning (SSL) methods are known to perform poorly on minority classes, we find that they still generate high precision pseudo-labels on minority classes. By exploiting this property, in this work, we propose Class-Rebalancing Self-Training (CReST), a simple yet effective framework to improve existing SSL methods on class-imbalanced data. CReST iteratively retrains a baseline SSL model with a labeled set expanded by adding pseudo labeled samples from an unlabeled set, where pseudo labeled samples from minority classes are selected more frequently according to an estimated class distribution. We also propose a progressive distribution alignment to adaptively adjust the rebalancing strength dubbed CReST+. We show that CReST and CReST+ improve state-of-the-art SSL algorithms on various class-imbalanced datasets and consistently outperform other popular rebalancing methods. Code has been made available at ht-sps//***/google-research/crest.

关键词： computer vision Adaptation models Codes Semisupervised learning 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 << < 444 445 446 447 448 449 450 451 452 453 > >>

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：