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

6,421 篇 会议
25 篇 期刊文献
3 册 图书

馆藏范围

6,448 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

3,849 篇 工学
- 3,647 篇 计算机科学与技术...
- 1,431 篇 软件工程
- 790 篇 光学工程
- 302 篇 信息与通信工程
- 242 篇 控制科学与工程
- 219 篇 电气工程
- 201 篇 机械工程
- 80 篇 生物医学工程（可授...
- 68 篇 生物工程
- 67 篇 电子科学与技术（可...
- 64 篇 仪器科学与技术
- 36 篇 建筑学
- 33 篇 力学（可授工学、理...
- 33 篇 土木工程
- 33 篇 航空宇航科学与技...
- 26 篇 安全科学与工程
- 22 篇 交通运输工程
- 20 篇 材料科学与工程（可...
- 18 篇 化学工程与技术
1,453 篇 理学
- 945 篇 物理学
- 890 篇 数学
- 352 篇 统计学（可授理学、...
- 134 篇 生物学
- 38 篇 系统科学
- 23 篇 化学
160 篇 管理学
- 110 篇 图书情报与档案管...
- 52 篇 管理科学与工程(可...
- 25 篇 工商管理
112 篇 医学
- 112 篇 临床医学
17 篇 法学
- 17 篇 社会学
12 篇 农学
8 篇 教育学
7 篇 艺术学
6 篇 经济学
2 篇 军事学

主题

2,288 篇 computer vision
789 篇 pattern recognit...
637 篇 cameras
629 篇 computer science
568 篇 face recognition
555 篇 layout
510 篇 image segmentati...
509 篇 conferences
498 篇 shape
445 篇 robustness
439 篇 object recogniti...
388 篇 humans
332 篇 feature extracti...
321 篇 training
303 篇 object detection
262 篇 image recognitio...
257 篇 application soft...
246 篇 lighting
238 篇 image reconstruc...
237 篇 computational mo...

机构

41 篇 microsoft resear...
26 篇 department of co...
21 篇 swiss fed inst t...
21 篇 school of comput...
20 篇 department of co...
19 篇 swiss fed inst t...
19 篇 carnegie mellon ...
18 篇 department of co...
17 篇 department of in...
17 篇 the robotics ins...
17 篇 institute of com...
16 篇 univ sci & techn...
16 篇 robotics institu...
15 篇 tsinghua univ pe...
14 篇 department of el...
14 篇 school of comput...
14 篇 school of comput...
13 篇 univ maryland co...
13 篇 microsoft resear...
13 篇 microsoft resear...

作者

39 篇 timofte radu
28 篇 s.k. nayar
24 篇 huang thomas s.
23 篇 xiaoou tang
22 篇 t. kanade
20 篇 t.s. huang
19 篇 van gool luc
19 篇 t. darrell
19 篇 chellappa rama
18 篇 nayar shree k.
17 篇 a.k. jain
17 篇 a. zisserman
17 篇 jain anil k.
16 篇 g. healey
16 篇 torralba antonio
16 篇 heung-yeung shum
16 篇 zisserman andrew
16 篇 l. van gool
15 篇 m. shah
15 篇 ji qiang

语言

6,447 篇 英文
2 篇 其他

检索条件"任意字段=1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1992"

共 6449 条记录，以下是901-910 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：

Multi-Scale Hourglass Hierarchical Fusion Network for Single Image Deraining

Multi-Scale Hourglass Hierarchical Fusion Network for Single...

引用

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

作者： Chen, Xiang Huang, Yufeng Xu, Lei Shenyang Aerosp Univ Coll Elect & Informat Engn Shenyang Liaoning Peoples R China Shenyang Fire Sci & Technol Res Inst MEM Shenyang Liaoning Peoples R China

ISBN: (纸本)9781665448994

Rain streaks bring serious blurring and visual quality degradation, which often vary in size, direction and density. Current CNN-based methods achieve encouraging performance, while are limited to depict rain characteristics and recover image details in the poor visibility environment. To address these issues, we present a Multi-scale Hourglass Hierarchical Fusion Network ((MHF)-F-2-Net) in end-to-end manner, to exactly captures rain streak features with multi-scale extraction, hierarchical distillation and information aggregation. For better extracting the features, a novel Multi-scale Hourglass Extraction Block (MHEB) is proposed to get local and global features across different scales through down- and up-sample process. Besides, a Hierarchical Attentive Distillation Block (HADB) then employs the dual attention feature responses to adaptively recalibrate the hierarchical features and eliminate the redundant ones. Further, we introduce a Residual Projected Feature Fusion (RPFF) strategy to progressively discriminate feature learning and aggregate different features instead of directly concatenating or adding. Extensive experiments on both synthetic and real rainy datasets demonstrate the effectiveness of the designed (MHF)-F-2-Net by comparing with recent state-of-the-art deraining algorithms.

关键词： Degradation Visualization computer vision Rain conferences Computational modeling Aggregates

来源：评论

学校读者我要写书评

暂无评论

Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scans

Domain adaptation, Explainability & Fairness in AI for Medic...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Dimitrios Kollias Anastasios Arsenos Stefanos Kollias Queen Mary University of London UK National Technical University of Athens Greece National Infrastructures for Research and Technology Greece

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The paper presents the DEF-AI-MIA COV19D Competition, which is organized in the framework of the ’Domain adaptation, Explainability, Fairness in AI for Medical Image Analysis (DEF-AI-MIA)’ Workshop of the 2024 computer vision and pattern recognition (cvpr) conference. The Competition is the 4th in the series, following the first three Competitions held in the framework of ICCV 2021, ECCV 2022 and ICASSP 2023 International conferences respectively. It includes two Challenges on: i) Covid-19 Detection and ii) Covid-19 Domain Adaptation. The Competition use data from COV19-CT-DB database, which is described in the paper and includes a large number of chest CT scan series. Each chest CT scan series consists of a sequence of 2-D CT slices, the number of which is between 50 and 700. Training, validation and test datasets have been extracted from COV19-CT-DB and provided to the participants in both Challenges. The paper presents the baseline models used in the Challenges and the performance which was obtained respectively, together with the best corresponding performances of the methods submitted and evaluated in the Challenges.

关键词： COVID-19 Training computer vision Image analysis Databases Computed tomography conferences

来源：评论

学校读者我要写书评

暂无评论

PLM: Partial Label Masking for Imbalanced Multi-label Classification

PLM: Partial Label Masking for Imbalanced Multi-label Classi...

引用

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

作者： Duarte, Kevin Rawat, Yogesh Shah, Mubarak Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (纸本)9781665448994

Neural networks trained on real-world datasets with long-tailed label distributions are biased towards frequent classes and perform poorly on infrequent classes. The imbalance in the ratio of positive and negative samples for each class skews network output probabilities further from ground-truth distributions. We propose a method, Partial Label Masking (PLM), which utilizes this ratio during training. By stochastically masking labels during loss computation, the method balances this ratio for each class, leading to improved recall on minority classes and improved precision on frequent classes. The ratio is estimated adaptively based on the network's performance by minimizing the KL divergence between predicted and ground-truth distributions. Whereas most existing approaches addressing data imbalance are mainly focused on single-label classification and do not generalize well to the multi-label case, this work proposes a general approach to solve the long-tail data imbalance issue for multi-label classification. PLM is versatile: it can be applied to most objective functions and it can be used alongside other strategies for class imbalance. Our method achieves strong performance when compared to existing methods on both multi-label (MultiMNIST and MSCOCO) and single-label (imbalanced CIFAR-10 and CIFAR-100) image classification datasets.

关键词： Training computer vision conferences Neural networks Linear programming pattern recognition Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

LEGAN: Disentangled Manipulation of Directional Lighting and Facial Expressions whilst Leveraging Human Perceptual Judgements

LEGAN: Disentangled Manipulation of Directional Lighting and...

引用

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

作者： Banerjee, Sandipan Joshi, Ajjen Mahajan, Prashant Bhattacharya, Sneha Kyal, Survi Mishra, Taniya Affectiva Boston MA 02109 USA Amazon Seattle WA USA Silver Spoon Animat Brooklyn NY USA SureStart San Francisco CA USA

ISBN: (纸本)9781665448994

Building facial analysis systems that generalize to extreme variations in lighting and facial expressions is a challenging problem that can potentially be alleviated using natural-looking synthetic data. Towards that, we propose LEGAN, a novel synthesis framework that leverages perceptual quality judgments for jointly manipulating lighting and expressions in face images, without requiring paired training data. LEGAN disentangles the lighting and expression subspaces and performs transformations in the feature space before upscaling to the desired output image. The fidelity of the synthetic image is further refined by integrating a perceptual quality estimation model, trained with face images rendered using multiple synthesis methods and their crowd-sourced naturalness ratings, into the LEGAN framework as an auxiliary discriminator. Using objective metrics like FID and LPIPS, LEGAN is shown to generate higher quality face images when compared with popular GAN models like StarGAN and StarGAN-v2 for lighting and expression synthesis. We also conduct a perceptual study using images synthesized by LEGAN and other GAN models and show the correlation between our quality estimation and visual fidelity. Finally, we demonstrate the effectiveness of LEGAN as training data augmenter for expression recognition and face verification tasks.

关键词： Measurement Visualization Face recognition Buildings Pipelines Lighting Training data

来源：评论

学校读者我要写书评

暂无评论

Cross Modality Knowledge Distillation for Multi-modal Aerial View Object Classification

Cross Modality Knowledge Distillation for Multi-modal Aerial...

引用

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

作者： Yang, Lehan Xu, Kele Univ Sydney Camperdown NSW Australia Key Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (纸本)9781665448994

In the case of bad weather or low lighting conditions, a single sensor may not be able to capture enough information for object identification. Compared with the traditional optical image, synthetic aperture radar (SAR) imaging has greater advantages, such as the ability to penetrate through fog and smoke. However, SAR images are of low resolution and contaminated by high-level speckle noise. As a result, it is of great difficulty to extract powerful and robust features from the SAR images. In this paper, we explored whether multiple imaging modalities can improve the object detection performance. Here, we propose a Cross Modality Knowledge Distillation (CMKD) paradigm, and explore two different network structures named CMKD-s and CMKD-m for the object classification task. Specifically, CMKD-s transfers the information captured by the two sensors using the online knowledge distillation, which can achieve cross-modal knowledge sharing and enhance the robustness of the aerial view object classification model. Moreover, leveraging the semi-supervised enhanced training, we proposed a novel method named CMKD-m, which strengthens the model for mutual knowledge transfer. Through quantitative comparison, we found that CMKD-s and CMKD-m outperform the method without knowledge transfer, on the NTIRE2021 SAR-EO challenge dataset.

关键词： Training Speckle Feature extraction Radar polarimetry Data models Robustness pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Detecting Anomalies in Semantic Segmentation with Prototypes

Detecting Anomalies in Semantic Segmentation with Prototypes

引用

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

作者： Fontanel, Dario Cermelli, Fabio Mancini, Massimiliano Caputo, Barbara Politecn Torino Turin Italy Italian Inst Technol Genoa Italy Univ Tubingen Tubingen Germany

ISBN: (纸本)9781665448994

Traditional semantic segmentation methods can recognize at test time only the classes that are present in the training set. This is a significant limitation, especially for semantic segmentation algorithms mounted on intelligent autonomous systems, deployed in realistic settings. Regardless of how many classes the system has seen at training time, it is inevitable that unexpected, unknown objects will appear at test time. The failure in identifying such anomalies may lead to incorrect, even dangerous behaviors of the autonomous agent equipped with such segmentation model when deployed in the real world. Current state of the art of anomaly segmentation uses generative models, exploiting their incapability to reconstruct patterns unseen during training. However, training these models is expensive, and their generated artifacts may create false anomalies. In this paper we take a different route and we propose to address anomaly segmentation through prototype learning. Our intuition is that anomalous pixels are those that are dissimilar to all class prototypes known by the model. We extract class prototypes from the training data in a lightweight manner using a cosine similarity-based classifier. Experiments on Street Hazards show that our approach achieves the new state of the art, with a significant margin over previous works, despite the reduced computational overhead.

关键词： Training Visualization computer vision Computational modeling conferences Semantics Prototypes

来源：评论

学校读者我要写书评

暂无评论

Spot the GEO Satellites: From Dataset to Kelvins SpotGEO Challenge

Spot the GEO Satellites: From Dataset to Kelvins SpotGEO Cha...

引用

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

作者： Chen, Bo Liu, Daqi Chin, Tat-Jun Rutten, Mark Derksenv, Dawa Martens, Marcus von Looz, Moritz Lecuyer, Gurvan Izzo, Dario Univ Adelaide Adelaide SA Australia InTrack Solut Adelaide SA Australia European Space Agcy Paris France

ISBN: (纸本)9781665448994

The Geosynchronous Equatorial Orbit (GEO) is home to many important space assets such as telecommunication and navigational satellites. Monitoring Resident Space Objects (RSOs) in GEO is a crucial aspect in achieving Space Situational Awareness (SSA) and in protecting critical space assets. However, ground-based GEO object detection is challenging due to the extreme distance of the targets, as well as nuisance factors including cloud coverage, atmospheric/weather effects, light pollution, sensor noise/defects, and star occlusions. The Kelvins SpotGEO Challenge is designed to establish to what extent images coming from a low-cost ground-based telescope can be used to detect GEO and near-GEO RSOs solely from photometric signals that are without any additional meta-data. At the same time, the SpotGEO dataset also addresses the lack of publicly available datasets from a computer vision perspective on the satellite detection problem;by assembling and releasing such a dataset, we hope to spur more efforts on the optical detection of RSOs and enable objective bench-marking for existing and future methods. In this work, we present details of the SpotGEO dataset development, challenge design, evaluation metric, and result analysis.

关键词： Space vehicles Measurement computer vision Satellites Pollution Kelvin Tools

来源：评论

学校读者我要写书评

暂无评论

Self-training Guided Adversarial Domain Adaptation For Thermal Imagery

Self-training Guided Adversarial Domain Adaptation For Therm...

引用

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

作者： Akkaya, Ibrahim Batuhan Altinel, Fazil Halici, Ugur Aselsan Inc Res Ctr Ankara Turkey Middle East Tech Univ Dept Elect & Elect Engn Ankara Turkey NOROM Neurosci & Neurotechnol Excellency Ctr Ankara Turkey

ISBN: (纸本)9781665448994

Deep models trained on large-scale RGB image datasets have shown tremendous success. It is important to apply such deep models to real-world problems. However, these models suffer from a performance bottleneck under illumination changes. Thermal IR cameras are more robust against such changes, and thus can be very useful for the real-world problems. In order to investigate efficacy of combining feature-rich visible spectrum and thermal image modalities, we propose an unsupervised domain adaptation method which does not require RGB-to-thermal image pairs. We employ large-scale RGB dataset MS-COCO as source domain and thermal dataset FLIR ADAS as target domain to demonstrate results of our method. Although adversarial domain adaptation methods aim to align the distributions of source and target domains, simply aligning the distributions cannot guarantee perfect generalization to the target domain. To this end, we propose a self-training guided adversarial domain adaptation method to promote generalization capabilities of adversarial domain adaptation methods. To perform self-training, pseudo labels are assigned to the samples on the target thermal domain to learn more generalized representations for the target domain. Extensive experimental analyses show that our proposed method achieves better results than the state-of-theart adversarial domain adaptation methods. The code and models are publicly available.(1)

关键词： Adaptation models computer vision conferences Lighting Cameras pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Contrastive Learning Improves Model Robustness Under Label Noise

Contrastive Learning Improves Model Robustness Under Label N...

引用

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

作者： Ghosh, Aritra Lan, Andrew Univ Massachusetts Amherst MA 01003 USA

ISBN: (纸本)9781665448994

Deep neural network-based classifiers trained with the categorical cross-entropy (CCE) loss are sensitive to label noise in the training data. One common type of method that can mitigate the impact of label noise can be viewed as supervised robust methods;one can simply replace the CCE loss with a loss that is robust to label noise, or re-weight training samples and down-weight those with higher loss values. Recently, another type of method using semi-supervised learning (SSL) has been proposed, which augments these supervised robust methods to exploit (possibly) noisy samples more effectively. Although supervised robust methods perform well across different data types, they have been shown to be inferior to the SSL methods on image classification tasks under label noise. Therefore, it remains to be seen that whether these supervised robust methods can also perform well if they can utilize the unlabeled samples more effectively. In this paper, we show that by initializing supervised robust methods using representations learned through contrastive learning leads to significantly improved performance under label noise. Surprisingly, even the simplest method (training a classifier with the CCE loss) can outperform the state-of-the-art SSL method by more than 50% under high label noise when initialized with contrastive learning. Our implementation will be publicly available at https://***/arghosh/noisy_label_pretrain.

关键词： Training Visualization conferences Training data Semisupervised learning Robustness pattern recognition

来源：评论

学校读者我要写书评

暂无评论

RUIG: Realistic Underwater Image Generation Towards Restoration

RUIG: Realistic Underwater Image Generation Towards Restorat...

引用

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

作者： Desai, Chaitra Tabib, Ramesh Ashok Reddy, Sai Sudheer Patil, Ujwala Mudenagudi, Uma KLE Technol Univ Ctr Excellence Visual Intelligence CEVI Hubballi Karnataka India

ISBN: (纸本)9781665448994

In this paper, we present a novel method for generating synthetic underwater images considering revised image formation model. We propose to use the generated synthetic underwater images to train a conditional generative adversarial network (CGAN) towards restoration of degraded underwater images. Restoration of degraded underwater images using traditional dehazing models is challenging as they are insensitive to wavelength, depth, water type and treat backscattering and direct signal attenuation coefficients to be equal. However, learning based models for restoration perform well but sensitive to availability of ground truth information. Generating ground truth labels in underwater scenario demands in-situ measurements using expensive equipments and is infeasible due to varying underwater currents. Towards this, we propose to generate synthetic underwater images using revised image formation model. Revised image formation model is sensitive to different attenuation coefficients: 1) back scattering, 2) direct scattering and 3) veiling light. We propose to estimate these attenuation coefficients considering proven facts from the literature. We demonstrate restoration of real underwater images through restoration framework trained using rendered synthetic underwater images, and compare results of restoration with state-of-the-art techniques.

关键词： computer vision Image synthesis Wavelength measurement Current measurement conferences Scattering Attenuation

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 << < 87 88 89 90 91 92 93 94 95 96 > >>

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：