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

5,347 篇 会议
123 册 图书
32 篇 期刊文献

馆藏范围

5,502 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

2,741 篇 工学
- 2,254 篇 计算机科学与技术...
- 925 篇 软件工程
- 478 篇 光学工程
- 394 篇 控制科学与工程
- 331 篇 机械工程
- 256 篇 仪器科学与技术
- 249 篇 信息与通信工程
- 130 篇 电气工程
- 122 篇 生物工程
- 90 篇 生物医学工程（可授...
- 70 篇 电子科学与技术（可...
- 39 篇 安全科学与工程
- 33 篇 化学工程与技术
- 30 篇 建筑学
- 27 篇 土木工程
- 25 篇 交通运输工程
- 24 篇 航空宇航科学与技...
858 篇 理学
- 526 篇 物理学
- 439 篇 数学
- 155 篇 统计学（可授理学、...
- 133 篇 生物学
- 28 篇 化学
- 21 篇 系统科学
237 篇 艺术学
- 237 篇 设计学（可授艺术学...
196 篇 管理学
- 101 篇 图书情报与档案管...
- 97 篇 管理科学与工程(可...
- 27 篇 工商管理
96 篇 医学
- 96 篇 临床医学
- 21 篇 基础医学(可授医学...
28 篇 法学
- 26 篇 社会学
8 篇 教育学
6 篇 经济学
5 篇 农学
2 篇 军事学

主题

2,166 篇 computer vision
915 篇 pattern recognit...
890 篇 conferences
640 篇 training
565 篇 cameras
410 篇 feature extracti...
373 篇 computational mo...
364 篇 image segmentati...
350 篇 visualization
338 篇 face recognition
321 篇 computer archite...
309 篇 robustness
290 篇 computer science
287 篇 shape
280 篇 humans
266 篇 object detection
234 篇 layout
196 篇 neural networks
191 篇 application soft...
191 篇 lighting

机构

20 篇 swiss fed inst t...
19 篇 institute for co...
18 篇 university of sc...
18 篇 microsoft resear...
18 篇 swiss fed inst t...
16 篇 carnegie mellon ...
16 篇 chinese acad sci...
14 篇 tsinghua univers...
14 篇 univ sci & techn...
14 篇 carnegie mellon ...
12 篇 harbin inst tech...
12 篇 tsinghua univ pe...
12 篇 department of co...
11 篇 megvii technol p...
11 篇 computer vision ...
11 篇 computer vision ...
10 篇 comp vis ctr bar...
10 篇 school of comput...
10 篇 univ modena & re...
10 篇 beihang univ peo...

作者

58 篇 timofte radu
18 篇 van gool luc
17 篇 luc van gool
17 篇 horst bischof
16 篇 rita cucchiara
16 篇 radu timofte
14 篇 escalera sergio
14 篇 bischof horst
14 篇 sergio escalera
13 篇 zhigang zhu
13 篇 hugo jair escala...
12 篇 murino vittorio
12 篇 li stan z.
12 篇 chen wei-ting
12 篇 qiang ji
11 篇 cucchiara rita
11 篇 fan haoqiang
11 篇 marcos v. conde
11 篇 bowyer kevin w.
11 篇 huang thomas s.

语言

5,490 篇 英文
10 篇 中文
3 篇 其他

检索条件"任意字段=2006 Conference on Computer Vision and Pattern Recognition Workshops"

共 5502 条记录，以下是4991-5000 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：

Efficient Search and Verification for Function Based Classification from Real Range Images

Efficient Search and Verification for Function Based Classif...

引用

International conference on pattern recognition

作者： I. Shimshoni E. Rivlin O. Soldea Department of Management Information Systems University of Haifa Haifa Israel Department of Computer Science Technion-Israel Institute of Technology Haifa Israel

In this work we propose a probabilistic model for generic object classification from raw range images. Our approach supports a validation process in which classes are verified using a functional class graph in which functional parts and their realization hypotheses are explored. The validation tree is efficiently searched. Some functional requirements are validated in a final procedure for more efficient separation of objects from non-objects. The search employs a knowledge repository mechanism that monotonically adds knowledge during the search and speeds up the classification process. Finally, we describe our implementation and present results of experiments on a database that comprises about one-hundred-and-fifty real raw range images of object instances from ten classes

关键词： Shape Image databases Object recognition pattern recognition Management information systems computer science Tree graphs computer vision System testing Stereo vision

来源：评论

学校读者我要写书评

暂无评论

Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation

Multi-Head Distillation for Continual Unsupervised Domain Ad...

引用

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

作者： Antoine Saporta Arthur Douillard Tuan-Hung Vu Patrick Pé rez Matthieu Cord Sorbonne Universit&#x00E9Valeo.ai Sorbonne Universit&#x00E9Heuritech Valeo.ai

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Unsupervised Domain Adaptation (UDA) is a transfer learning task which aims at training on an unlabeled target domain by leveraging a labeled source domain. Beyond the traditional scope of UDA with a single source domain and a single target domain, real-world perception systems face a variety of scenarios to handle, from varying lighting conditions to many cities around the world. In this context, UDAs with several domains increase the challenges with the addition of distribution shifts within the different target domains. This work focuses on a novel framework for learning UDA, continuous UDA, in which models operate on multiple target domains discovered sequentially, without access to previous target domains. We propose MuHDi, for Multi-Head Distillation, a method that solves the catastrophic forgetting problem, inherent in continual learning tasks. MuHDi performs distillation at multiple levels from the previous model as well as an auxiliary target-specialist segmentation head. We report both extensive ablation and experiments on challenging multi-target UDA semantic segmentation benchmarks to validate the proposed learning scheme and architecture.

关键词： Training Adaptation models Urban areas Semantics Transfer learning computer architecture Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Tri-VAE: Triplet Variational Autoencoder for Unsupervised Anomaly Detection in Brain Tumor MRI

Tri-VAE: Triplet Variational Autoencoder for Unsupervised An...

引用

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

作者： Hansen Wijanarko Evelyne Calista Li-Fen Chen Yong-Sheng Chen National Yang Ming Chiao Tung University Taiwan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The intricate manifestations of pathological brain lesions in imaging data pose challenges for supervised detection methods due to the scarcity of annotated samples. To overcome this difficulty, our focus shifts to unsupervised anomaly detection. In this work, we exclusively train the proposed model using healthy data to identify unseen anomalies during testing. This study entails investigating the triplet-based variational autoencoder to simultaneously learn the distribution of healthy brain data and denoising capabilities. Importantly, we rectify a misconception inherent in prior projection-based approaches which relies on the presumption that healthy regions within images would persist unaltered in the reconstructed output. This inadvertently implied a substantial likeness in latent space representations between lesion and lesion-free images. However, this assumption might not hold true, particularly due to the potential significant impact of lesion area intensities on the projection process notably for autoencoders with single information bottleneck. To overcome this limitation, we disentangled metric learning from latent sampling. This approach ensures that both lesion and lesion-free input images are projected into the same distribution, specifically the lesion-free projection. Moreover, we introduce a semantic-guided gated cross skip module to enhance spatial detail retrieval while suppressing anomalies, leveraging robust healthy brain representation semantics exist in the deeper levels of the decoder. We also discovered that incorporating structure similarity index measure as an extra training objective bolsters the capability of anomaly detection for the proposed model.

关键词： Training Measurement Pathology Semantics Noise reduction Brain modeling pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Deep Lighting Environment Map Estimation from Spherical Panoramas

Deep Lighting Environment Map Estimation from Spherical Pano...

引用

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

作者： Vasileios Gkitsas Nikolaos Zioulis Federico Alvarez Dimitrios Zarpalas Petros Daras Centre for Research and Technology Hellas (CERTH) Information Technologies Institute (ITI) Signals Systems and Radiocommunications Department (SSRD) Universidad Politecnica de Madrid (UPM)

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Estimating a scene's lighting is a very important task when compositing synthetic content within real environments, with applications in mixed reality and postproduction. In this work we present a data-driven model that estimates an HDR lighting environment map from a single LDR monocular spherical panorama. In addition to being a challenging and ill-posed problem, the lighting estimation task also suffers from a lack of facile illumination ground truth data, a fact that hinders the applicability of data-driven methods. We approach this problem differently, exploiting the availability of surface geometry to employ image-based relighting as a data generator and supervision mechanism. This relies on a global Lambertian assumption that helps us overcome issues related to pre-baked lighting. We relight our training data and complement the model's supervision with a photometric loss, enabled by a differentiable image-based relighting technique. Finally, since we predict spherical spectral coefficients, we show that by imposing a distribution prior on the predicted coefficients, we can greatly boost performance. Code and models available at ***/DeepPanoramaLighting.

关键词： Lighting Estimation Task analysis Geometry Data models Image color analysis Rendering (computer graphics)

来源：评论

学校读者我要写书评

暂无评论

TherISuRNet - A Computationally Efficient Thermal Image Super-Resolution Network

TherISuRNet - A Computationally Efficient Thermal Image Supe...

引用

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

作者： Vishal Chudasama Heena Patel Kalpesh Prajapati Kishor Upla Raghavendra Ramachandra Kiran Raja Christoph Busch Sardar Vallabhbhai National Institute of Technology (SVNIT) Surat India Norwegian University of Science and Technology (NTNU) Gjøvik Norway

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Human perception is limited to perceive the objects beyond the range of visible wavelengths in the Electromagnetic (EM) spectrum. This prevents them to recognize objects in different conditions such as poor illumination or severe weather (e.g., under fog or smoke). The technological advancement in thermographic imaging enables the visualization of objects beyond visible range which enables it's use in many applications such as military, medical, agriculture, etc. However, due to the hardware constraints, the thermal cameras are limited with poor spatial resolution when compared to similar visible range RGB cameras. In this paper, we propose a Super-Resolution (SR) of thermal images using a deep neural network architecture which we refer to as TherISuRNet. We use progressive upscaling strategy with asymmetrical residual learning in the network which is computationally efficient for different upscaling factors such as ×2, ×3 and ×4. The proposed architecture consists of different modules for low and high-frequency feature extraction along with upsampling blocks. The effectiveness of the proposed architecture in TherISuRNet is verified by evaluating it with different datasets. The obtained results indicate superior results as compared to other state-of-the-art SR methods.

关键词： Cameras Spatial resolution Feature extraction Training computer architecture

来源：评论

学校读者我要写书评

暂无评论

Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles

Deep-Learning-Based Aerial Image Classification for Emergenc...

引用

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

作者： Christos Kyrkou Theocharis Theocharides KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Unmanned Aerial Vehicles (UAVs), equipped with camera sensors can facilitate enhanced situational awareness for many emergency response and disaster management applications since they are capable of operating in remote and difficult to access areas. In addition, by utilizing an embedded platform and deep learning UAVs can autonomously monitor a disaster stricken area, analyze the image in real-time and alert in the presence of various calamities such as collapsed buildings, flood, or fire in order to faster mitigate their effects on the environment and on human population. To this end, this paper focuses on the automated aerial scene classification of disaster events from on-board a UAV. Specifically, a dedicated Aerial Image Database for Emergency Response (AIDER) applications is introduced and a comparative analysis of existing approaches is performed. Through this analysis a lightweight convolutional neural network (CNN) architecture is developed, capable of running efficiently on an embedded platform achieving ~3x higher performance compared to existing models with minimal memory requirements with less than 2% accuracy drop compared to the state-of-the-art. These preliminary results provide a solid basis for further experimentation towards real-time aerial image classification for emergency response applications using UAVs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

From 2D Portraits to 3D Realities: Advancing GAN Inversion for Enhanced Image Synthesis

From 2D Portraits to 3D Realities: Advancing GAN Inversion f...

引用

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

作者： Wonseok Oh Youngjoo Jo Electrical and Computer Engineering University of Michigan ETRI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image synthesis using StyleGAN has shown remarkable results in 2D portrait image generation. The works of the GAN inversion to manipulate the real image using StyleGAN latent space also show remarkable achievements. 2D GAN inversion has successfully manipulated global attributes such as facial expressions and gender. However, preserving the hairstyle and identity was difficult according to the pose change. We introduce the 3D GAN inversion encoder to make a high-resolution 3D image based on the Geometry Aware 3D Generative Adversarial Network, known as EG3D, which allows explicit control over the pose of the real image subject with multi-view consistency. Our network projects the single 2D portrait images to novel latent space for 3D GAN inversion for the tri-plane of EG3D. We also present multi-view cycle loss, which aims to increase multi-view consistency. By leveraging the new latent space and loss for 3D GAN inversion, our network can successfully convert 2D portrait images into 3D fast.

关键词： Visualization Three-dimensional displays Image synthesis Computational modeling Semantics Aerospace electronics Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Salient Object-Aware Background Generation using Text-Guided Diffusion Models

Salient Object-Aware Background Generation using Text-Guided...

引用

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

作者： Amir Erfan Eshratifar João V. B. Soares Kapil Thadani Shaunak Mishra Mikhail Kuznetsov Yueh-Ning Ku Paloma De Juan Yahoo Research Amazon ByteDance

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Generating background scenes for salient objects plays a crucial role across various domains including creative design and e-commerce, as it enhances the presentation and context of subjects by integrating them into tailored environments. Background generation can be framed as a task of text-conditioned outpainting, where the goal is to extend image content beyond a salient object’s boundaries on a blank background. Although popular diffusion models for text-guided inpainting can also be used for outpainting by mask inversion, they are trained to fill in missing parts of an image rather than to place an object into a scene. Consequently, when used for background creation, inpainting models frequently extend the salient object’s boundaries and thereby change the object’s identity, which is a phenomenon we call "object expansion." This paper introduces a model for adapting inpainting diffusion models to the salient object outpainting task using Stable Diffusion and ControlNet architectures. We present a series of qualitative and quantitative results across models and datasets, including a newly proposed metric to measure object expansion that does not require any human labeling. Compared to Stable Diffusion 2.0 Inpainting, our proposed approach reduces object expansion by 3.6× on average with no degradation in standard visual metrics across multiple datasets.

关键词： Measurement Degradation Adaptation models Visualization conferences Diffusion models pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Is Synthetic Data all We Need? Benchmarking the Robustness of Models Trained with Synthetic Images

Is Synthetic Data all We Need? Benchmarking the Robustness o...

引用

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

作者： Krishnakant Singh Thanush Navaratnam Jannik Holmer Simone Schaub-Meyer Stefan Roth Department of Computer Science TU Darmstadt Hessian.AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

A long-standing challenge in developing machine learning approaches has been the lack of high-quality labeled data. Recently, models trained with purely synthetic data, here termed synthetic clones, generated using large-scale pre-trained diffusion models have shown promising results in overcoming this annotation bottleneck. As these synthetic clone models progress, they are likely to be deployed in challenging real-world settings, yet their suitability remains understudied. Our work addresses this gap by providing the first benchmark for three classes of synthetic clone models, namely supervised, self-supervised, and multi-modal ones, across a range of robustness measures. We show that existing synthetic self-supervised and multi-modal clones are comparable to or outperform state-of-the-art real-image baselines for a range of robustness metrics – shape bias, background bias, calibration, etc. However, we also find that synthetic clones are much more susceptible to adversarial and real-world noise than models trained with real data. To address this, we find that combining both real and synthetic data further increases the robustness, and that the choice of prompt used for generating synthetic images plays an important part in the robustness of synthetic clones.

关键词： Measurement Analytical models Shape Noise Cloning Benchmark testing Robustness

来源：评论

学校读者我要写书评

暂无评论

Pairwise Teacher-Student Network for Semi-Supervised Hashing

Pairwise Teacher-Student Network for Semi-Supervised Hashing

引用

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

作者： Shifeng Zhang Jianmin Li Bo Zhang Department of Computer Science and Technology Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Hashing method maps similar high-dimensional data to binary hashcodes with smaller hamming distance, and it has received broad attention due to its low storage cost and fast retrieval speed. Pairwise similarity is easily obtained and widely used for retrieval, and most supervised hashing algorithms are carefully designed for the pairwise supervisions. As labeling all data pairs is difficult, semi-supervised hashing is proposed which aims at learning efficient codes with limited labeled pairs and abundant unlabeled ones. Existing methods build graphs to capture the structure of dataset, but they are not working well for complex data as the graph is built based on the data representations and determining the representations of complex data is difficult. In this paper, we propose a novel teacher-student semi-supervised hashing framework in which the student is trained with the pairwise information produced by the teacher network. The network follows the smoothness assumption, which achieves consistent distances for similar data pairs so that the retrieval results are similar for neighborhood queries. Experiments on large-scale datasets show that the proposed method reaches impressive gain over the supervised baselines and is superior to state-of-the-art semi-supervised hashing methods.

关键词： Training Hamming distance Semantics Data models Semisupervised learning Perturbation methods Labeling

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 << < 491 492 493 494 495 496 497 498 499 500 > >>

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：