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

22,771 篇 会议
112 篇 期刊文献
3 册 图书

馆藏范围

22,885 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,398 篇 工学
- 10,880 篇 计算机科学与技术...
- 3,450 篇 软件工程
- 2,430 篇 机械工程
- 1,721 篇 光学工程
- 1,010 篇 控制科学与工程
- 998 篇 电气工程
- 761 篇 信息与通信工程
- 393 篇 仪器科学与技术
- 337 篇 生物工程
- 257 篇 生物医学工程（可授...
- 215 篇 电子科学与技术（可...
- 113 篇 化学工程与技术
- 112 篇 安全科学与工程
- 98 篇 测绘科学与技术
- 92 篇 交通运输工程
- 86 篇 建筑学
- 82 篇 土木工程
3,362 篇 医学
- 3,348 篇 临床医学
- 79 篇 基础医学(可授医学...
3,250 篇 理学
- 1,953 篇 物理学
- 1,664 篇 数学
- 567 篇 统计学（可授理学、...
- 484 篇 生物学
- 245 篇 系统科学
- 109 篇 化学
506 篇 管理学
- 299 篇 图书情报与档案管...
- 219 篇 管理科学与工程(可...
- 75 篇 工商管理
252 篇 艺术学
- 252 篇 设计学（可授艺术学...
62 篇 法学
- 59 篇 社会学
40 篇 农学
25 篇 教育学
19 篇 经济学
11 篇 军事学
3 篇 文学

主题

10,126 篇 computer vision
4,008 篇 pattern recognit...
2,900 篇 training
1,958 篇 computational mo...
1,792 篇 cameras
1,758 篇 visualization
1,485 篇 shape
1,466 篇 image segmentati...
1,447 篇 feature extracti...
1,412 篇 three-dimensiona...
1,288 篇 robustness
1,169 篇 computer archite...
1,144 篇 layout
1,142 篇 computer science
1,134 篇 semantics
1,071 篇 object detection
1,043 篇 conferences
1,009 篇 benchmark testin...
967 篇 codes
810 篇 face recognition

机构

135 篇 univ sci & techn...
118 篇 univ chinese aca...
118 篇 chinese univ hon...
110 篇 carnegie mellon ...
99 篇 tsinghua univers...
98 篇 microsoft resear...
94 篇 swiss fed inst t...
92 篇 zhejiang univ pe...
82 篇 university of sc...
81 篇 zhejiang univers...
78 篇 university of ch...
77 篇 shanghai ai lab ...
72 篇 shanghai jiao to...
68 篇 microsoft res as...
65 篇 national laborat...
65 篇 alibaba grp peop...
64 篇 tsinghua univ pe...
63 篇 adobe research
60 篇 peking univ peop...
59 篇 peng cheng labor...

作者

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

语言

22,824 篇 英文
35 篇 其他
20 篇 中文
5 篇 土耳其文
2 篇 日文

检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22886 条记录，以下是4731-4740 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：

Face recognition using deep learning feature injection: An accurate hybrid network combining neural networks based on feature extraction with convolutional neural network 48

Face recognition using deep learning feature injection: An a...

引用

48th Latin American computer conference (CLEI)

作者： Sisco, Yilber J. Carmona, Rhadames Univ Cent Venezuela Escuela Comp Ctr Comp Graf Caracas Venezuela

ISBN: (数字)9781665476713

ISBN: (纸本)9781665476713

In this work we evaluate, compare and combine several face recognition models. Three multi-layer neural networks are trained with different descriptors: Histograms of Gradient Orientation (HOG), Scale Invariant Feature Transform (SIFT) and Local Binary pattern Histograms (LBPH). A fourth model uses a Convolutional Neural Networks (CNN), trained with the raw images. The combination of these networks into a hybrid network results in a noticeable accuracy improvement, not only by comparing the results with the individual networks, but also with respect to the current state of the art. To validade our hybrid model, we include different sets of images, acquired under different conditions such as lighting, pose and expression.

关键词： computer vision Machine Learning Histograms of Gradient Orientation (HOG) Scale Invariant Feature Transformations (SIFT) Local Binary pattern Histograms (LBPH) Convolutional Neural Network Artificial Intelligence Transfer Learning Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Unpaired Image-to-Image Translation via Latent Energy Transport

Unpaired Image-to-Image Translation via Latent Energy Transp...

引用

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

作者： Zhao, Yang Chen, Changyou Univ Buffalo SUNY Buffalo NY 14260 USA

ISBN: (纸本)9781665445092

Image-to-image translation aims to preserve source contents while translating to discriminative target styles between two visual domains. Most works apply adversarial learning in the ambient image space, which could be computationally expensive and challenging to train. In this paper, we propose to deploy an energy-based model (EBM) in the latent space of a pretrained autoencoder for this task. The pretrained autoencoder serves as both a latent code extractor and an image reconstruction worker. Our model, LETIT1, is based on the assumption that two domains share the same latent space, where latent representation is implicitly decomposed as a content code and a domain-specific style code. Instead of explicitly extracting the two codes and applying adaptive instance normalization to combine them, our latent EBM can implicitly learn to transport the source style code to the target style code while preserving the content code, an advantage over existing image translation methods. This simplified solution is also more efficient in the one-sided unpaired image translation setting. Qualitative and quantitative comparisons demonstrate superior translation quality and faithfulness for content preservation. Our model is the first to be applicable to 1024x1024-resolution unpaired image translation to the best of our knowledge. Code is available at https://***/YangNaruto/latentenergy-transport.

关键词： Visualization computer vision Adaptation models Codes Computational modeling Scalability Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder

Soft-IntroVAE: Analyzing and Improving the Introspective Var...

引用

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

作者： Daniel, Tal Tamar, Aviv Technion Dept Elect Engn Haifa Israel

ISBN: (纸本)9781665445092

The recently introduced introspective variational autoencoder (IntroVAE) exhibits outstanding image generations, and allows for amortized inference using an image encoder. The main idea in IntroVAE is to train a VAE adversarially, using the VAE encoder to discriminate between generated and real data samples. However, the original IntroVAE loss function relied on a particular hinge-loss formulation that is very hard to stabilize in practice, and its theoretical convergence analysis ignored important terms in the loss. In this work, we take a step towards better understanding of the IntroVAE model, its practical implementation, and its applications. We propose the Soft-IntroVAE, a modified IntroVAE that replaces the hinge-loss terms with a smooth exponential loss on generated samples. This change significantly improves training stability, and also enables theoretical analysis of the complete algorithm. Interestingly, we show that the IntroVAE converges to a distribution that minimizes a sum of KL distance from the data distribution and an entropy term. We discuss the implications of this result, and demonstrate that it induces competitive image generation and reconstruction. Finally, we describe an application of Soft-IntroVAE to unsupervised image translation, and demonstrate compelling results. Code and additional information is available on the project website - ***/soft-intro-vae-web.

关键词： Training computer vision Codes Image synthesis Computational modeling Stability analysis Entropy

来源：评论

学校读者我要写书评

暂无评论

Feature Decomposition and Reconstruction Learning for Effective Facial Expression recognition

Feature Decomposition and Reconstruction Learning for Effect...

引用

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

作者： Ruan, Delian Yan, Yan Lai, Shenqi Chai, Zhenhua Shen, Chunhua Wang, Hanzi Xiamen Univ Xiamen Peoples R China Meituan Vis Intelligence Ctr Beijing Peoples R China Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781665445092

In this paper, we propose a novel Feature Decomposition and Reconstruction Learning (FDRL) method for effective facial expression recognition. We view the expression information as the combination of the shared information (expression similarities) across different expressions and the unique information (expression-specific variations) for each expression. More specifically, FDRI, mainly consists of two crucial networks: a Feature Decomposition Network (FDN) and a Feature Reconstruction Network (MN). In particular, FDN first decomposes the basic features extracted from a backbone network into a set of facial action-aware latent features to model expression similarities. Then, FRN captures the intra-feature and inter feature relationships for latent features to characterize expression-specific variations, and reconstructs the expression feature. To this end, two modules including an intra-feature relation modeling module and an inter-feature relation modeling module are developed in FRN. Experimental results on both the in-the-lab databases (including CK+, MMI, and Oulu-CASIA) and the in-the-wild databases (including RAF-DB and SFEW) show that the proposed FDRL method consistently achieves higher recognition accuracy than several state-of-the-art methods. This clearly highlights the benefit of feature decomposition and reconstruction for classifying expressions.

关键词： computer vision Databases Face recognition Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Multi-modal Aerial View Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results - PBVS 2023

Multi-modal Aerial View Image Challenge: Translation from Sy...

引用

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

作者： Spencer Low Oliver Nina Angel D. Sappa Erik Blasch Nathan Inkawhich Brigham Young University Provo Utah Air Force Research Laboratory Dayton OH ESPOL Polytechnic University Ecuador Computer Vision Center Spain Air Force Research Laboratory Arlington VA Air Force Research Laboratory Rome NY

This paper unveils the discoveries and outcomes of the inaugural iteration of the Multi-modal Aerial View Image Challenge (MAVIC) aimed at image translation. The primary objective of this competition is to stimulate research efforts towards the development of models capable of translating co-aligned images between multiple modalities. To accomplish the task of image translation, the competition utilizes images obtained from both synthetic aperture radar (SAR) and electro-optical (EO) sources. Specifically, the challenge centers on the translation from the SAR modality to the EO modality, an area of research that has garnered attention. The inaugural challenge demonstrates the feasibility of the task. The dataset utilized in this challenge is derived from the UNIfied COincident Optical and Radar for recognition (UNICORN) dataset. We introduce an new version of the UNICORN dataset that is focused on enabling the sensor translation task. Performance evaluation is conducted using a combination of measures to ensure high fidelity and high accuracy translations.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Learning a Proposal Classifier for Multiple Object Tracking

Learning a Proposal Classifier for Multiple Object Tracking

引用

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

作者： Dai, Peng Weng, Renliang Choi, Wongun Zhang, Changshui He, Zhangping Ding, Wei Tsinghua Univ Beijing Peoples R China Aibee Inc Beijing Peoples R China

ISBN: (纸本)9781665445092

The recent trend in multiple object tracking (MOT) is heading towards leveraging deep learning to boost the tracking performance. However, it is not trivial to solve the data-association problem in an end-to-end fashion. In this paper, we propose a novel proposal-based learnable framework, which models MOT as a proposal generation, proposal scoring and trajectory inference paradigm on an affinity graph. This framework is similar to the two-stage object detector Faster RCNN, and can solve the MOT problem in a data-driven way. For proposal generation, we propose an iterative graph clustering method to reduce the computational cost while maintaining the quality of the generated proposals. For proposal scoring, we deploy a trainable graph-convolutional-network (GCN) to learn the structural patterns of the generated proposals and rank them according to the estimated quality scores. For trajectory inference, a simple deoverlapping strategy is adopted to generate tracking output while complying with the constraints that no detection can be assigned to more than one track. We experimentally demonstrate that the proposed method achieves a clear performance improvement in both MOTA and IDF1 with respect to previous state-of-the-art on two public benchmarks.

关键词： Deep learning computer vision Detectors Market research Trajectory Computational efficiency Proposals

来源：评论

学校读者我要写书评

暂无评论

Semantic Image Matting

Semantic Image Matting

引用

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

作者： Sun, Yanan Tang, Chi-Keung Tai, Yu-Wing HKUST Hong Kong Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

Natural image matting separates the foreground from background in fractional occupancy which can be caused by highly transparent objects, complex foreground (e.g., net or tree), and/or objects containing very fine details (e.g., hairs). Although conventional matting formulation can be applied to all of the above cases, no previous work has attempted to reason the underlying causes of matting due to various foreground semantics. We show how to obtain better alpha mattes by incorporating into our framework semantic classification of matting regions. Specifically, we consider and learn 20 classes of matting patterns, and propose to extend the conventional trimap to semantic trimap. The proposed semantic trimap can be obtained automatically through patch structure analysis within trimap regions. Meanwhile, we learn a multi-class discriminator to regularize the alpha prediction at semantic level, and content-sensitive weights to balance different regularization losses. Experiments on multiple benchmarks show that our method outperforms other methods and has achieved the most competitive state-of-theart performance. Finally, we contribute a large-scale Semantic Image Matting Dataset with careful consideration of data balancing across different semantic classes. Code and dataset are available at https://***/nowsyn/ SIM.

关键词： Hair computer vision Codes Semantics Benchmark testing Data mining Optimization

来源：评论

学校读者我要写书评

暂无评论

3D-MAN: 3D Multi-frame Attention Network for Object Detection

3D-MAN: 3D Multi-frame Attention Network for Object Detectio...

引用

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

作者： Yang, Zetong Zhou, Yin Chen, Zhifeng Ngiam, Jiquan Chinese Univ Hong Kong Hong Kong Peoples R China Waymo LLC Mountain View CA USA Google Res Brain Team Mountain View CA USA

ISBN: (纸本)9781665445092

3D object detection is an important module in autonomous driving and robotics. However, many existing methods focus on using single frames to perform 3D detection, and do not fully utilize information from multiple frames. In this paper, we present 3D-MAN: a 3D multi-frame attention network that effectively aggregates features from multiple perspectives and achieves state-of-the-art performance on Waymo Open Dataset. 3D-MAN first uses a novel fast single-frame detector to produce box proposals. The box proposals and their corresponding feature maps are then stored in a memory bank. We design a multi-view alignment and aggregation module, using attention networks, to extract and aggregate the temporal features stored in the memory bank. This effectively combines the features coming from different perspectives of the scene. We demonstrate the effectiveness of our approach on the large-scale complex Waymo Open Dataset, achieving state-of-the-art results compared to published single-frame and multi-frame methods.

关键词： computer vision Three-dimensional displays Fuses Aggregates Object detection Detectors Feature extraction

来源：评论

学校读者我要写书评

暂无评论

M³P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training

M<SUP>3</SUP>P: Learning Universal Representations via Multi...

引用

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

作者： Ni, Minheng Huang, Haoyang Su, Lin Cui, Edward Bharti, Taroon Wang, Lijuan Zhang, Dongdong Duan, Nan Harbin Inst Technol Res Ctr Social Comp & Informat Retrieval Harbin Peoples R China Microsoft Res Asia Nat Language Comp Shanghai Peoples R China Microsoft Bing Multimedia Team Shanghai Peoples R China Microsoft Cloud AI Redmond WA USA

ISBN: (纸本)9781665445092

We present (MP)-P-3, a Multitask Multilingual Multimodal Pre-trained model that combines multilingual pre-training and multimodal pre-training into a unified framework via multitask pre-training. Our goal is to learn universal representations that can map objects occurred in different modalities or texts expressed in different languages into a common semantic space. In addition, to explicitly encourage fine-grained alignment between images and non-English languages, we also propose Multimodal Code-switched Training (MCT) to combine monolingual pre-training and multimodal pre-training via a code-switch strategy. Experiments are performed on the multilingual image retrieval task across two benchmark datasets, including MSCOCO and Multi3OK. (MP)-P-3 can achieve comparable results for English and new state-of-the-art results for non-English languages.

关键词： Training computer vision Computational modeling Semantics Image retrieval Benchmark testing Data models

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Learning of Depth and Depth-of-Field Effect from Natural Images with Aperture Rendering Generative Adversarial Networks

Unsupervised Learning of Depth and Depth-of-Field Effect fro...

引用

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

作者： Kaneko, Takuhiro NTT Corp NTT Commun Sci Labs Tokyo Japan

ISBN: (纸本)9781665445092

Understanding the 3D world from 2D projected natural images is a fundamental challenge in computer vision and graphics. Recently, an unsupervised learning approach has garnered considerable attention owing to its advantages in data collection. However, to mitigate training limitations, typical methods need to impose assumptions for viewpoint distribution (e.g., a dataset containing various viewpoint images) or object shape (e.g., symmetric objects). These assumptions often restrict applications;for instance, the application to non-rigid objects or images captured from similar viewpoints (e.g., flower or bird images) remains a challenge. To complement these approaches, we propose aperture rendering generative adversarial networks (AR-GANs), which equip aperture rendering on top of GANs, and adopt focus cues to learn the depth and depth-of-field (DoF) effect of unlabeled natural images. To address the ambiguities triggered by unsupervised setting (i.e., ambiguities between smooth texture and out-of-focus blurs, and between foreground and background blurs), we develop DoF mixture learning, which enables the generator to learn real image distribution while generating diverse DoF images. In addition, we devise a center focus prior to guiding the learning direction. In the experiments, we demonstrate the effectiveness of AR-GANs in various datasets, such as flower, bird, and face images, demonstrate their portability by incorporating them into other 3D representation learning GANs, and validate their applicability in shallow DoF rendering.

关键词： Training computer vision Three-dimensional displays Shape Computational modeling Apertures Rendering (computer graphics)

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 << < 470 471 472 473 474 475 476 477 478 479 > >>

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：