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

3,310 篇 会议
3 篇 期刊文献

馆藏范围

3,313 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,936 篇 工学
- 1,843 篇 计算机科学与技术...
- 201 篇 软件工程
- 147 篇 机械工程
- 134 篇 光学工程
- 41 篇 生物工程
- 28 篇 信息与通信工程
- 18 篇 电气工程
- 13 篇 控制科学与工程
- 9 篇 电子科学与技术（可...
- 9 篇 化学工程与技术
- 9 篇 交通运输工程
- 8 篇 生物医学工程（可授...
- 7 篇 安全科学与工程
- 4 篇 材料科学与工程（可...
- 4 篇 建筑学
- 3 篇 土木工程
- 3 篇 农业工程
360 篇 医学
- 359 篇 临床医学
- 3 篇 基础医学(可授医学...
178 篇 理学
- 137 篇 物理学
- 42 篇 生物学
- 30 篇 数学
- 16 篇 统计学（可授理学、...
- 10 篇 化学
- 3 篇 系统科学
14 篇 管理学
- 7 篇 管理科学与工程(可...
- 7 篇 图书情报与档案管...
- 3 篇 工商管理
5 篇 法学
- 3 篇 社会学
- 2 篇 法学
2 篇 教育学
2 篇 农学
1 篇 经济学

主题

1,738 篇 computer vision
900 篇 training
802 篇 conferences
643 篇 pattern recognit...
482 篇 computational mo...
431 篇 computer archite...
430 篇 task analysis
426 篇 visualization
350 篇 feature extracti...
317 篇 semantics
308 篇 three-dimensiona...
243 篇 neural networks
227 篇 benchmark testin...
222 篇 cameras
183 篇 image segmentati...
177 篇 estimation
168 篇 deep learning
156 篇 measurement
154 篇 object detection
151 篇 data models

机构

40 篇 univ sci & techn...
31 篇 peng cheng lab p...
29 篇 swiss fed inst t...
29 篇 sensetime res pe...
27 篇 university of sc...
27 篇 zhejiang univ pe...
26 篇 univ chinese aca...
26 篇 swiss fed inst t...
25 篇 university of ch...
24 篇 tsinghua univ pe...
24 篇 univ chinese aca...
24 篇 nanyang technol ...
24 篇 sun yat sen univ...
22 篇 peng cheng labor...
20 篇 shanghai ai lab ...
20 篇 korea adv inst s...
19 篇 chinese univ hon...
19 篇 yonsei univ
18 篇 peking univ peop...
18 篇 tsinghua univers...

作者

65 篇 timofte radu
21 篇 loy chen change
18 篇 van gool luc
18 篇 radu timofte
16 篇 zha zheng-jun
14 篇 sun jian
12 篇 fan haoqiang
12 篇 chen wei-ting
12 篇 lei lei
12 篇 qiao yu
11 篇 zheng wei-shi
11 篇 zheng-jun zha
11 篇 liu shuaicheng
11 篇 qi tian
11 篇 luc van gool
11 篇 marcos v. conde
11 篇 liu shuai
11 篇 van de weijer jo...
10 篇 danelljan martin
10 篇 kim seon joo

语言

3,311 篇 英文
2 篇 其他

检索条件"任意字段=2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2020"

共 3313 条记录，以下是541-550 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：

Masked vision Transformers for Hyperspectral Image Classification

Masked Vision Transformers for Hyperspectral Image Classific...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Scheibenreif, Linus Mommert, Michael Borth, Damian University of St. Gallen Aiml Lab School of Computer Science Switzerland

ISBN: (纸本)9798350302493

Transformer architectures have become state-of-the-art models in computer vision and natural language processing. To a significant degree, their success can be attributed to self-supervised pre-training on large scale unlabeled datasets. This work investigates the use of self-supervised masked image reconstruction to advance transformer models for hyperspectral remote sensing imagery. To facilitate self-supervised pre-training, we build a large dataset of unlabeled hyperspectral observations from the EnMAP satellite and systematically investigate modifications of the vision transformer architecture to optimally leverage the characteristics of hyperspectral data. We find significant improvements in accuracy on different land cover classification tasks over both standard vision and sequence transformers using (i) blockwise patch embeddings, (ii) spatialspectral self-attention, (iii) spectral positional embeddings and (iv) masked self-supervised pre-training1. The resulting model outperforms standard transformer architectures by +5% accuracy on a labeled subset of our EnMAP data and by +15% on Houston2018 hyperspectral dataset, making it competitive with a strong 3D convolutional neural network baseline. In an ablation study on label-efficiency based on the Houston2018 dataset, self-supervised pre-training significantly improves transformer accuracy when little labeled training data is available. The self-supervised model outperforms randomly initialized transformers and the 3D convolutional neural network by +7-8% when only 0.1-10% of the training labels are available. © 2023 ieee.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Towards Automated Polyp Segmentation Using Weakly- and Semi-Supervised Learning and Deformable Transformers

Towards Automated Polyp Segmentation Using Weakly- and Semi-...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Ren, Guangyu Lazarou, Michalis Yuan, Jing Stathaki, Tania Imperial College London United Kingdom

ISBN: (纸本)9798350302493

Polyp segmentation is a crucial step towards computer-aided diagnosis of colorectal cancer. However, most of the polyp segmentation methods require pixel-wise annotated datasets. Annotated datasets are tedious and time-consuming to produce, especially for physicians who must dedicate their time to their patients. To this end, we propose a novel weakly- and semi-supervised learning polyp segmentation framework that can be trained using only weakly annotated images along with unlabeled images making it very cost-efficient to use. More specifically our contributions are: 1) a novel weakly annotated polyp dataset, 2) a novel sparse foreground loss that suppresses false positives and improves weakly-supervised training, 3) a deformable transformer encoder neck for feature enhancement by fusing information across levels and flexible spatial *** experimental results demonstrate the merits of our ideas on five challenging datasets outperforming some state-of-the-art fully supervised models. Also, our framework can be utilized to fine-tune models trained on natural image segmentation datasets drastically improving their performance for polyp segmentation and impressively demonstrating superior performance to fully supervised fine-tuning. Code can be found in https://***/ic-qialanqian/WS-DefSegNet. © 2023 ieee.

关键词： computer aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

RB-Dust - A Reference-based Dataset for vision-based Dust Removal

RB-Dust - A Reference-based Dataset for Vision-based Dust Re...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Buckel, Peter Oksanen, Timo Dietmueller, Thomas Ravensburg Germany Technical University of Munich Germany Germany

ISBN: (纸本)9798350302493

Dust in the agricultural landscape is a significant challenge and influences, for example, the environmental perception of autonomous agricultural machines. Image enhancement algorithms can be used to reduce dust. However, these require dusty and dust-free images of the same environment for validation. In fact, to date, there is no dataset that we are aware of that addresses this issue. Therefore, we present the agriscapes RB-Dust dataset, which is named after its purpose of reference-based dust removal. It is not possible to take pictures from the cabin during tillage, as this would cause shifts in the images. Because of this, we built a setup from which it is possible to take images from a stationary position close to the passing tractor. The test setup was based on a half-sided gate through which the tractor could drive. The field tests were carried out on a farm in Bavaria, Germany, during tillage. During the field tests, other parameters such as soil moisture and wind speed were controlled, as these significantly affect dust development. We validated our dataset with contrast enhancement and image dehazing algorithms and analyzed the generalizability from recordings from the moving tractor. Finally, we demonstrate the application of dust removal based on a high-level vision task, such as person classification. Our empirical study confirms the validity of RB-Dust for vision-based dust removal in agriculture. © 2023 ieee.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Underwater Light Field Retention : Neural Rendering for Underwater Imaging

Underwater Light Field Retention : Neural Rendering for Unde...

引用

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

作者： Ye, Tian Chen, Sixiang Liu, Yun Ye, Yi Chen, Erkang Li, Yuche Jimei Univ Sch Ocean Informat Engn Xiamen Peoples R China Southwest Univ Coll Artificial Intelligence Chongqing Peoples R China China Univ Petr Coll Geosci Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Underwater Image Rendering aims to generate a true-to-life underwater image from a given clean one, which could be applied to various practical applications such as underwater image enhancement, camera filter, and virtual gaming. We explore two less-touched but challenging problems in underwater image rendering, namely, i) how to render diverse underwater scenes by a single neural network? ii) how to adaptively learn the underwater light fields from natural exemplars, i,e., realistic underwater images? To this end, we propose a neural rendering method for underwater imaging, dubbed UWNR (Underwater Neural Rendering). Specifically, UWNR is a data-driven neural network that implicitly learns the natural degenerated model from authentic underwater images, avoiding introducing erroneous biases by hand-craft imaging models. Compared with existing underwater image generation methods, UWNR utilizes the natural light field to simulate the main characteristics of the underwater scene. Thus, it is able to synthesize a wide variety of underwater images from one clean image with various realistic underwater images. Extensive experiments demonstrate that our approach achieves better visual effects and quantitative metrics over previous methods. Moreover, we adopt UWNR to build an open Large Neural Rendering Underwater Dataset containing various types of water quality, dubbed LNRUD. The source code and LNRUD are available at https://***/Ephemeral182/UWNR. [GRAPHICS] .

关键词： Measurement Image synthesis conferences Neural networks Water quality Rendering (computer graphics) Visual effects

来源：评论

学校读者我要写书评

暂无评论

ECO: Ensembling Context Optimization for vision-Language Models

ECO: Ensembling Context Optimization for Vision-Language Mod...

引用

ieee/cvf International conference on computer vision (ICCV)

作者： Agnolucci, Lorenzo Baldrati, Alberto Todino, Francesco Becattini, Federico Bertini, Marco Del Bimbo, Alberto Univ Florence Florence Italy Univ Siena Siena Italy Univ Pisa Pisa Italy

ISBN: (纸本)9798350307443

Image recognition has recently witnessed a paradigm shift, where vision-language models are now used to perform few-shot classification based on textual prompts. Among these, the CLIP model has shown remarkable capabilities for zero-shot transfer by matching an image and a custom textual prompt in its latent space. This has paved the way for several works that focus on engineering or learning textual contexts for maximizing CLIP's classification capabilities. In this paper, we follow this trend by learning an ensemble of prompts for image classification. We show that learning diverse and possibly shorter contexts improves considerably and consistently the results rather than relying on a single trainable prompt. In particular, we report better few-shot capabilities with no additional cost at inference time. We demonstrate the capabilities of our approach on 11 different benchmarks.

关键词： CLIP CoOP Few shot classification Few shot learning ImageNet Prompt ensembling Prompt learning vision and Language

来源：评论

学校读者我要写书评

暂无评论

Light Field Synthesis from a Monocular Image using Variable LDI

Light Field Synthesis from a Monocular Image using Variable ...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Bak, Junhyeong Kyu Park, In Inha University Department of Electrical and Computer Engineering Incheon22212 Korea Republic of

ISBN: (纸本)9798350302493

Recent advancements in learning-based novel view synthesis enable users to synthesize light field from a monocular image without special equipment. Moreover, the state-of-the-art techniques including multiplane image (MPI) show outstanding performance in synthesizing accurate light field from a monocular image. In this study, we propose a new variable layered depth image (VLDI) representation to generate precise light field synthesis results using only a few layers. Our method exploits LDI representation built on a new two-stream halfway fusion network and transformation process. This framework has an efficient structure that directly generates the region that does not require network prediction from inputs. As a result, the proposed method allows us to acquire high-quality light field easily and quickly. Experimental results show that the proposed method outperforms the previous works quantitatively and qualitatively for diverse examples. © 2023 ieee.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Cali-NCE: Boosting Cross-modal Video Representation Learning with Calibrated Alignment

Cali-NCE: Boosting Cross-modal Video Representation Learning...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Zhao, Nanxuan Jiao, Jianbo Xie, Weidi Lin, Dahua University of Bath Department of Computer Science United Kingdom University of Birmingham School of Computer Science United Kingdom Shanghai Jiaotong University Cooperative Medianet Innovation Center China Chinese University of Hong Kong Department of Information Engineering Hong Kong

ISBN: (纸本)9798350302493

With the large-scale video-text datasets being collected, learning general visual-textual representation has gained increasing attention. While recent methods are designed with the assumption that the alt-text description naturally conveys the meaning and context of the video in semantics (i.e. well aligned with each other), it is unlikely to be satisfied for the Internet data, which potentially harms the quality of the learned visual-textual representation. To address this challenge, we first revisit three mainstream approaches: correspondence modeling, contrastive learning and predictive coding, demonstrating that a simple co-training strategy with these methods leads to a clear improvement in performance. To further explore the complementary nature of different training strategies, we propose a simple yet effective joint training framework that factorizes the total objective into conditional ones, termed as Cali-NCE 1. Our method first estimates confidence scores for measuring the correspondence between video and text descriptions, and the scores are later used to calibrate the sample weightings during contrastive training. Through extensive experiments, we show that the proposed approach achieves state-of-the-art performance on multiple downstream tasks: text-to-video retrieval, video action recognition, and video retrieval. © 2023 ieee.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry

H-Net: Unsupervised Attention-based Stereo Depth Estimation ...

引用

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

作者： Huang, Baoru Zheng, Jian-Qing Giannarou, Stamatia Elson, Daniel S. Imperial Coll London Hamlyn Ctr Robot Surg London England Univ Oxford Kennedy Inst Rheumatol Oxford England Univ Oxford Big Data Inst Oxford England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Depth estimation from a stereo image pair has become one of the most explored applications in computer vision, with most previous methods relying on fully supervised learning settings. However, due to the difficulty in acquiring accurate and scalable ground truth data, the training of fully supervised methods is challenging. As an alternative, self-supervised methods are becoming more popular to mitigate this challenge. In this paper, we introduce the H-Net, a deep-learning framework for unsupervised stereo depth estimation that leverages epipolar geometry to refine stereo matching. For the first time, a Siamese autoencoder architecture is used for depth estimation which allows mutual information between rectified stereo images to be extracted. To enforce the epipolar constraint, the mutual epipolar attention mechanism has been designed which gives more emphasis to correspondences of features that lie on the same epipolar line while learning mutual information between the input stereo pair. Stereo correspondences are further enhanced by incorporating semantic information to the proposed attention mechanism. More specifically, the optimal transport algorithm is used to suppress attention and eliminate outliers in areas not visible in both cameras. Extensive experiments on KITTI2015 and Cityscapes show that the proposed modules are able to improve the performance of the unsupervised stereo depth estimation methods while closing the gap with the fully supervised approaches.

关键词： Geometry Training computer vision Supervised learning Semantics Estimation computer architecture

来源：评论

学校读者我要写书评

暂无评论

LSDIR: A Large Scale Dataset for Image Restoration

LSDIR: A Large Scale Dataset for Image Restoration

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Li, Yawei Zhang, Kai Liang, Jingyun Cao, Jiezhang Liu, Ce Gong, Rui Zhang, Yulun Tang, Hao Liu, Yun Demandolx, Denis Ranjan, Rakesh Timofte, Radu Van Gool, Luc Computer Vision Lab Eth Zürich Switzerland Meta Reality Labs United States University of Würzburg Germany Ku Leuven Belgium

ISBN: (纸本)9798350302493

The aim of this paper is to propose a large scale dataset for image restoration (LSDIR). Recent work in image restoration has been focused on the design of deep neural networks. The datasets used to train these networks 'only' contain some thousands of images, which is still incomparable with the large scale datasets for other vision tasks such as visual recognition and object detection. The small training set limits the performance of image restoration networks. To solve this problem, we collect high-resolution (HR) images from Flickr for image restoration. To ensure the pixel-level quality of the collected dataset, annotators were invited to manually inspect each of the collected image and remove the low-quality ones. The final dataset contains 84,991 high-quality training images, 1,000 validation images, and 1,000 test images. In addition, we showed that the model capacity of large networks could be fully exploited by training on the large scale dataset with significantly increased patch size and prolonged training iterations. The experimental results on image super-resolution (SR), denoising, JPEG deblocking, deblurring, and demosaicking, and real-world SR show that image restoration networks benefit a lot from the large scale dataset. © 2023 ieee.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

The 6th AI City Challenge

The 6th AI City Challenge

引用

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

作者： Naphade, Milind Wang, Shuo Anastasiu, David C. Tang, Zheng Chang, Ming-Ching Yao, Yue Zheng, Liang Rahman, Mohammed Shaiqur Venkatachalapathy, Archana Sharma, Anuj Feng, Qi Ablavsky, Vitaly Sclaroff, Stan Chakraborty, Pranamesh Li, Alice Li, Shangru Chellappa, Rama NVIDIA Corp Santa Clara CA 95051 USA Santa Clara Univ Santa Clara CA 95053 USA SUNY Albany Albany NY 12222 USA Australian Natl Univ Canberra ACT Australia Indian Inst Technol Kanpur Kanpur Uttar Pradesh India Iowa State Univ Ames IA USA Boston Univ Boston MA 02215 USA Univ Washington Seattle WA 98195 USA Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The 6th edition of the AI City Challenge specifically focuses on problems in two domains where there is tremendous unlocked potential at the intersection of computer vision and artificial intelligence: Intelligent Traffic Systems (ITS), and brick and mortar retail businesses. The four challenge tracks of the 2022 AI City Challenge received participation requests from 254 teams across 27 countries. Track 1 addressed city-scale multi-target multi-camera (MTMC) vehicle tracking. Track 2 addressed natural-language-based vehicle track retrieval. Track 3 was a brand new track for naturalistic driving analysis, where the data were captured by several cameras mounted inside the vehicle focusing on driver safety, and the task was to classify driver actions. Track 4 was another new track aiming to achieve retail store automated checkout using only a single view camera. We released two leader boards for submissions based on different methods, including a public leader board for the contest, where no use of external data is allowed, and a general leader board for all submitted results. The top performance of participating teams established strong baselines and even outperformed the state-of-the-art in the proposed challenge tracks.

关键词： computer vision Mortar Urban areas Focusing Cameras Safety 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332页 << < 51 52 53 54 55 56 57 58 59 60 > >>

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：