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

23,001 篇 会议
126 册 图书
92 篇 期刊文献

馆藏范围

23,218 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,623 篇 工学
- 11,108 篇 计算机科学与技术...
- 3,479 篇 软件工程
- 2,445 篇 机械工程
- 1,716 篇 光学工程
- 1,075 篇 电气工程
- 1,014 篇 控制科学与工程
- 785 篇 信息与通信工程
- 412 篇 仪器科学与技术
- 352 篇 生物工程
- 251 篇 生物医学工程（可授...
- 196 篇 电子科学与技术（可...
- 114 篇 化学工程与技术
- 108 篇 安全科学与工程
- 100 篇 测绘科学与技术
- 88 篇 建筑学
- 87 篇 交通运输工程
- 84 篇 土木工程
3,494 篇 医学
- 3,481 篇 临床医学
- 81 篇 基础医学(可授医学...
3,242 篇 理学
- 1,939 篇 物理学
- 1,640 篇 数学
- 563 篇 统计学（可授理学、...
- 500 篇 生物学
- 249 篇 系统科学
- 107 篇 化学
522 篇 管理学
- 311 篇 图书情报与档案管...
- 224 篇 管理科学与工程(可...
- 76 篇 工商管理
276 篇 艺术学
- 276 篇 设计学（可授艺术学...
66 篇 法学
- 63 篇 社会学
38 篇 农学
28 篇 教育学
22 篇 经济学
10 篇 军事学
3 篇 文学

主题

10,187 篇 computer vision
3,967 篇 pattern recognit...
3,005 篇 training
2,007 篇 computational mo...
1,818 篇 visualization
1,815 篇 cameras
1,516 篇 feature extracti...
1,481 篇 shape
1,455 篇 three-dimensiona...
1,438 篇 image segmentati...
1,287 篇 robustness
1,205 篇 computer archite...
1,155 篇 semantics
1,147 篇 conferences
1,107 篇 layout
1,092 篇 computer science
1,087 篇 object detection
1,025 篇 benchmark testin...
970 篇 codes
922 篇 face recognition

机构

136 篇 univ sci & techn...
121 篇 univ chinese aca...
118 篇 chinese univ hon...
107 篇 carnegie mellon ...
101 篇 tsinghua univers...
101 篇 microsoft resear...
95 篇 swiss fed inst t...
93 篇 zhejiang univ pe...
82 篇 university of sc...
81 篇 zhejiang univers...
80 篇 university of ch...
77 篇 shanghai ai lab ...
72 篇 shanghai jiao to...
69 篇 national laborat...
67 篇 microsoft res as...
67 篇 alibaba grp peop...
64 篇 adobe research
61 篇 tsinghua univ pe...
60 篇 peking univ peop...
59 篇 univ oxford oxfo...

作者

81 篇 van gool luc
72 篇 timofte radu
64 篇 zhang lei
47 篇 luc van gool
40 篇 yang yi
40 篇 li stan z.
37 篇 loy chen change
34 篇 chen chen
33 篇 xiaoou tang
32 篇 liu yang
32 篇 qi tian
31 篇 tian qi
31 篇 sun jian
30 篇 murino vittorio
30 篇 pascal fua
29 篇 darrell trevor
29 篇 li fei-fei
28 篇 li xin
28 篇 ying shan
27 篇 vasconcelos nuno

语言

23,137 篇 英文
53 篇 其他
22 篇 中文
5 篇 土耳其文
2 篇 日文

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

共 23219 条记录，以下是581-590 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：

When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search

When NAS Meets Trees: An Efficient Algorithm for Neural Arch...

引用

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

作者： Qian, Guocheng Zhang, Xuanyang Li, Guohao Zhao, Chen Chen, Yukang Zhang, Xiangyu Ghanem, Bernard Sun, Jian King Abdullah Univ Sci & Technol KAUST Thuwal Saudi Arabia MEGVII Technol Beijing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The key challenge in neural architecture search (NAS) is designing how to explore wisely in the huge search space. We propose a new NAS method called TNAS (NAS with trees), which improves search efficiency by exploring only a small number of architectures while also achieving a higher search accuracy. TNAS introduces an architecture tree and a binary operation tree, to factorize the search space and substantially reduce the exploration size. TNAS performs a modified bi-level Breadth-First Search in the proposed trees to discover a high-performance architecture. Impressively, TNAS finds the global optimal architecture on CIFAR-10 with test accuracy of 94.37% in four GPU hours in NAS-Bench-201. The average test accuracy is 94.35%, which outperforms the state-of-the-art. Code is available at: https://***/guochengqian/TNAS.

关键词： computer vision Costs Codes conferences Graphics processing units computer architecture pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Self-supervised vision Transformers for Land-cover Segmentation and Classification

Self-supervised Vision Transformers for Land-cover Segmentat...

引用

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

作者： Scheibenreif, Linus Hanna, Joelle Mommert, Michael Borth, Damian Univ St Gallen AIML Lab Sch Comp Sci Rosenbergstr 30 St Gallen Switzerland

ISBN: (纸本)9781665487399

Transformer models have recently approached or even surpassed the performance of ConvNets on computer vision tasks like classification and segmentation. To a large degree, these successes have been enabled by the use of large-scale labelled image datasets for supervised pre-training. This poses a significant challenge for the adaption of vision Transformers to domains where datasets with millions of labelled samples are not available. In this work, we bridge the gap between ConvNets and Transformers for Earth observation by self-supervised pre-training on large-scale unlabelled remote sensing data(1). We show that self-supervised pre-training yields latent task-agnostic representations that can be utilized for both land cover classification and segmentation tasks, where they significantly outperform the fully supervised baselines. Additionally, we find that subsequent fine-tuning of Transformers for specific downstream tasks performs on-par with commonly used ConvNet architectures. An ablation study further illustrates that the labelled dataset size can be reduced to one-tenth after self-supervised pre-training while still maintaining the performance of the fully supervised approach.

关键词： Training Earth computer vision Image segmentation Computational modeling conferences Transformers

来源：评论

学校读者我要写书评

暂无评论

Disentangled Loss for Low-Bit Quantization-Aware Training

Disentangled Loss for Low-Bit Quantization-Aware Training

引用

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

作者： Allenet, Thibault Briand, David Bichler, Olivier Sentieys, Olivier CEA LIST Saclay France Univ Rennes INRIA Rennes France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Quantization-Aware Training (QAT) has recently showed a lot of potential for low-bit settings in the context of image classification. Approaches based on QAT are using the Cross Entropy Loss function which is the reference loss function in this domain. We investigate quantization-aware training with disentangled loss functions. We qualify a loss to disentangle as it encourages the network output space to be easily discriminated with linear functions. We introduce a new method, Disentangled Loss Quantization Aware Training, as our tool to empirically demonstrate that the quantization procedure benefits from those loss functions. Results show that the proposed method substantially reduces the loss in top-1 accuracy for low-bit quantization on CIFAR10, CIFAR100 and ImageNet. Our best result brings the top-1 Accuracy of a Resnet-18 from 63.1% to 64.0% with binary weights and 2-bit activations when trained on ImageNet.

关键词： Training Visualization computer vision Quantization (signal) conferences Entropy pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Learning to Name Classes for vision and Language Models

Learning to Name Classes for Vision and Language Models

引用

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

作者： Parisot, Sarah Yang, Yongxin McDonagh, Steven Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges that remain however, are high sensitivity to the choice of handcrafted class names that define queries, and the difficulty of adaptation to new, smaller datasets. Towards addressing these problems, we propose to leverage available data to learn, for each class, an optimal word embedding as a function of the visual content. By learning new word embeddings on an otherwise frozen model, we are able to retain zero-shot capabilities for new classes, easily adapt models to new datasets, and adjust potentially erroneous, non-descriptive or ambiguous class names. We show that our solution can easily be integrated in image classification and object detection pipelines, yields significant performance gains in multiple scenarios and provides insights into model biases and labelling errors.

关键词： language reasoning vision

来源：评论

学校读者我要写书评

暂无评论

EKILA: Synthetic Media Provenance and Attribution for Generative Art

EKILA: Synthetic Media Provenance and Attribution for Genera...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Balan, Kar Agarwal, Shruti Jenni, Simon Parsons, Andy Gilbert, Andrew Collomosse, John University of Surrey United Kingdom Adobe Inc.

ISBN: (纸本)9798350302493

We present EKILA;a decentralized framework that enables creatives to receive recognition and reward for their contributions to generative AI (GenAI). EKILA proposes a robust visual attribution technique and combines this with an emerging content provenance standard (C2PA) to address the problem of synthetic image provenance - determining the generative model and training data responsible for an AI-generated image. Furthermore, EKILA extends the non-fungible token (NFT) ecosystem to introduce a tokenized representation for rights, enabling a triangular relationship between the asset's Ownership, Rights, and Attribution (ORA). Leveraging the ORA relationship enables creators to express agency over training consent and, through our attribution model, to receive apportioned credit, including royalty payments for the use of their assets in GenAI. © 2023 ieee.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Initialization Noise in Image Gradients and Saliency Maps

Initialization Noise in Image Gradients and Saliency Maps

引用

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

作者： Woerl, Ann-Christin Disselhoff, Jan Wand, Michael Johannes Gutenberg Univ Mainz Inst Comp Sci Mainz Germany

ISBN: (纸本)9798350301298

In this paper, we examine gradients of logits of image classification CNNs by input pixel values. We observe that these fluctuate considerably with training randomness, such as the random initialization of the networks. We extend our study to gradients of intermediate layers, obtained via GradCAM, as well as popular network saliency estimators such as DeepLIFT, SHAP, LIME, Integrated Gradients, and SmoothGrad. While empirical noise levels vary, qualitatively different attributions to image features are still possible with all of these, which comes with implications for interpreting such attributions, in particular when seeking data-driven explanations of the phenomenon generating the data. Finally, we demonstrate that the observed artefacts can be removed by marginalization over the initialization distribution by simple stochastic integration.

关键词： Explainable computer vision

来源：评论

学校读者我要写书评

暂无评论

Out-Of-Distribution Detection In Unsupervised Continual Learning

Out-Of-Distribution Detection In Unsupervised Continual Lear...

引用

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

作者： He, Jiangpeng Zhu, Fengqing Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Unsupervised continual learning aims to learn new tasks incrementally without requiring human annotations. However, most existing methods, especially those targeted on image classification, only work in a simplified scenario by assuming all new data belong to new tasks, which is not realistic if the class labels are not provided. Therefore, to perform unsupervised continual learning in real life applications, an out-of-distribution detector is required at beginning to identify whether each new data corresponds to a new task or already learned tasks, which still remains under-explored yet. In this work, we formulate the problem for Out-of-distribution Detection in Unsupervised Continual Learning (OOD-UCL) with the corresponding evaluation protocol. In addition, we propose a novel OOD detection method by correcting the output bias at first and then enhancing the output confidence for in-distribution data based on task discriminativeness, which can be applied directly without modifying the learning procedures and objectives of continual learning. Our method is evaluated on CIFAR-100 dataset by following the proposed evaluation protocol and we show improved performance compared with existing OOD detection methods under the unsupervised continual learning scenario.

关键词： computer vision Protocols Annotations conferences Detectors pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

FairCLIP: Harnessing Fairness in vision-Language Learning

FairCLIP: Harnessing Fairness in Vision-Language Learning

引用

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

作者： Luol, Yan Shil, Min Khan, Muhammad Osama Afzal, Muhammad Muneeb Huang, Hao Yuan, Shuaihang Tian, Yu Song, Luo Kouhana, Ava Elze, Tobias Fang, Yi Wang, Mengyu Harvard Univ Harvard Ophthalmol AI Lab Cambridge MA 02138 USA NYU Tandon Sch Engn New York NY USA New York Univ Abu Dhabi Multimedia & Visual Comp Lab Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350353006

Fairness is a critical concern in deep learning, especially in healthcare, where these models influence diagnoses and treatment decisions. Although fairness has been investigated in the vision-only domain, the fairness of medical vision-language (VL) models remains unexplored due to the scarcity of medical VL datasets for studying fairness. To bridge this research gap, we introduce the first fair vision-language medical dataset (Harvard-FairVLMed) that provides detailed demographic attributes, ground-truth labels, and clinical notes to facilitate an in-depth examination of fairness within VL foundation models. Using Harvard-FairVLMed, we conduct a comprehensive fairness analysis of two widely-used VL models (CLIP and BLIP2), pre-trained on both natural and medical domains, across four different protected attributes. Our results highlight significant biases in all VL models, with Asian, Male, Non-Hispanic, and Spanish being the preferred subgroups across the protected attributes of race, gender, ethnicity, and language, respectively. In order to alleviate these biases, we propose FairCLIP, an optimal-transport-based approach that achieves a favorable trade-off between performance and fairness by reducing the Sinkhorn distance between the overall sample distribution and the distributions corresponding to each demographic group. As the first VL dataset of its kind, Harvard-FairVLMed holds the potential to catalyze advancements in the development of machine learning models that are both ethically aware and clinically effective. Our dataset and code are available at https://***/datasets/

关键词： AI for Medicine Equitable Deep Learning Fairness Learning Large Language Models Medical AI Ophthalmology Optimal Transport vision-Language Learning

来源：评论

学校读者我要写书评

暂无评论

Enriched Robust Multi-View Kernel Subspace Clustering

Enriched Robust Multi-View Kernel Subspace Clustering

引用

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

作者： Zhang, Mengyuan Liu, Kai Clemson Univ Clemson SC 29631 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Subspace clustering is to find underlying low-dimensional subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. Most existing methods suffer from two critical issues. First, they usually adopt a two-stage framework and isolate the processes of affinity learning, multi-view information fusion and clustering. Second, they assume the data lies in a linear subspace which may fail in practice as most real-world datasets may have non-linearity structures. To address the above issues, in this paper we propose a novel Enriched Robust Multi-View Kernel Subspace Clustering framework where the consensus affinity matrix is learned from both multi-view data and spectral clustering. Due to the objective and constraints which is difficult to optimize, we propose an iterative optimization method which is easy to implement and can yield closed solution in each step. Extensive experiments have validated the superiority of our method over state-of-the-art clustering methods.

关键词： computer vision Clustering methods conferences Optimization methods Benchmark testing Iterative methods Kernel

来源：评论

学校读者我要写书评

暂无评论

Attenuating Catastrophic Forgetting by Joint Contrastive and Incremental Learning

Attenuating Catastrophic Forgetting by Joint Contrastive and...

引用

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

作者： Ferdinand, Quentin Clement, Benoit Oliveau, Quentin Le Chenadec, Gilles Papadakis, Panagiotis Naval Grp Res Cherbourg En Cotentin France ENSTA Bretagne Lab STICC UMR 6285 Brest France IMT Atlantique Lab STICC UMR 6285 Brest France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In class incremental learning, discriminative models are trained to classify images while adapting to new instances and classes incrementally. Training a model to adapt to new classes without total access to previous class data, however, leads to the known problem of catastrophic forgetting of the previously learnt classes. To alleviate this problem, we show how we can build upon recent progress on contrastive learning methods. In particular, we develop an incremental learning approach for deep neural networks operating both at classification and representation level which alleviates forgetting and learns more general features for data classification. Experiments performed on several datasets demonstrate the superiority of the proposed method with respect to well known state-of-the-art methods.

关键词： Learning systems Training Deep learning Adaptation models computer vision conferences Computational modeling

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 << < 55 56 57 58 59 60 61 62 63 64 > >>

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：