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

22,772 篇 会议
112 篇 期刊文献
23 册 图书

馆藏范围

22,906 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

13,399 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
- 1,011 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

10,127 篇 computer vision
4,025 篇 pattern recognit...
2,900 篇 training
1,958 篇 computational mo...
1,793 篇 cameras
1,759 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...
99 篇 microsoft resear...
94 篇 swiss fed inst t...
92 篇 zhejiang univ pe...
82 篇 university of sc...
81 篇 zhejiang univers...
77 篇 shanghai ai lab ...
77 篇 university of ch...
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 xiaoou tang
33 篇 li stan z.
33 篇 qi tian
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

22,845 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 22907 条记录，以下是4611-4620 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 Multi-Lens Bokeh Effect Rendering and Transformation

Efficient Multi-Lens Bokeh Effect Rendering and Transformati...

引用

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

作者： Tim Seizinger Marcos V. Conde Manuel Kolmet Tom E. Bishop Radu Timofte Computer Vision Lab CAIDAS & IFI University of Würzburg Germany Glass Imaging Inc. CA

Many advancements of mobile cameras aim to reach the visual quality of professional DSLR cameras. Great progress was shown over the last years in optimizing the sharp regions of an image and in creating virtual portrait effects with artificially blurred backgrounds. Bokeh is the aesthetic quality of the blur in out-of-focus areas of an image. This is a popular technique among professional photographers, and for this reason, a new goal in computational photography is to optimize the Bokeh effect *** paper introduces EBokehNet, a efficient state-of-the-art solution for Bokeh effect transformation and rendering. Our method can render Bokeh from an all-in-focus image, or transform the Bokeh of one lens to the effect of another lens without harming the sharp foreground regions in the image. Moreover we can control the shape and strength of the effect by feeding the lens properties i.e. type (Sony or Canon) and aperture, into the neural network as an additional input. Our method is a winning solution at the NTIRE 2023 Lens-to-Lens Bokeh Effect Transformation Challenge, and state-of-the-art at the EBB benchmark.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards Learning Image Similarity from General Triplet Labels

Towards Learning Image Similarity from General Triplet Label...

引用

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

作者： Radu Dondera Greenfield Vision

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Metric learning for images has so far focused overwhelmingly on a class-based definition of similarity: two images are similar if they belong to the same class and dissimilar otherwise. Impressive results were achieved on datasets for fine grained categorization, but performance is nearing saturation. Recent work in neuroscience and psychology produced datasets with other types of similarity labels, e.g. the outlier in a group of three, but traditional metric learning methods are ill-suited to such data because of low density of labels. To overcome this difficulty, we propose a novel approach in the teacher-student learning paradigm. Multiple teacher models learn to embed images based only on relations with other images, and then a student model learns to embed images based on both content and dense relations provided by the teachers. We show significant improvement over existing triplet based metric learning methods, both in result quality and in training efficiency. Additionally, through experiments on class based datasets, we show the generality of approaching metric learning via knowledge transfer. Code is available at ***/greenfieldvision/tdml.

关键词： Measurement Training Learning systems Visualization Neuroscience Psychology Focusing

来源：评论

学校读者我要写书评

暂无评论

Domain Targeted Synthetic Plant Style Transfer using Stable Diffusion, LoRA and ControlNet

Domain Targeted Synthetic Plant Style Transfer using Stable ...

引用

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

作者： Zane K. J. Hartley Rob J. Lind Michael P. Pound Andrew P. French University of Nottingham Syngenta Jealott’s Hill International Research Centre

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Synthetic images can help alleviate much of the cost in the creation of training data for plant phenotyping-focused AI development. Synthetic-to-real style transfer is of particular interest to users of artificial data because of the domain shift problem created by training neural networks on images generated in a digital environment. In this paper we present a pipeline for synthetic plant creation and image-to-image style transfer, with a particular interest in synthetic to real domain adaptation targeting specific real datasets. Utilizing new advances in generative AI, we employ a combination of Stable diffusion, Low Ranked Adapters (LoRA) and ControlNets to produce an advanced system of style transfer. We focus our work on the core task of leaf instance segmentation, exploring both synthetic to real style transfer as well as inter-species style transfer and find that our pipeline makes numerous improvements over CycleGAN for style transfer, and the images we produce are comparable to real images when used as training data.

关键词： Training Instance segmentation computer vision Costs Generative AI conferences Pipelines

来源：评论

学校读者我要写书评

暂无评论

PointNetLK Revisited

PointNetLK Revisited

引用

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

作者： Li, Xueqian Pontes, Jhony Kaesemodel Lucey, Simon Argo AI Pittsburgh PA 15222 USA Univ Adelaide Adelaide SA Australia Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665445092

We address the generalization ability of recent learning-based point cloud registration methods. Despite their success, these approaches tend to have poor performance when applied to mismatched conditions that are not well-represented in the training set, such as unseen object categories, different complex scenes, or unknown depth sensors. In these circumstances, it has often been better to rely on classical non-learning methods (e.g., Iterative Closest Point), which have better generalization ability. Hybrid learning methods, that use learning for predicting point correspondences and then a deterministic step for alignment, have offered some respite, but are still limited in their generalization abilities. We revisit a recent innovation-PointNetLK [1]-and show that the inclusion of an analytical Jacobian can exhibit remarkable generalization properties while reaping the inherent fidelity benefits of a learning framework. Our approach not only outperforms the state-of-the-art in mismatched conditions but also produces results competitive with current learning methods when operating on real-world test data close to the training set.

关键词： Training Jacobian matrices Solid modeling Three-dimensional displays Robustness Sensors pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Efficient Conditional GAN Transfer with Knowledge Propagation across Classes

Efficient Conditional GAN Transfer with Knowledge Propagatio...

引用

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

作者： Shahbazi, Mohamad Huang, Zhiwu Paudel, Danda Pani Chhatkuli, Ajad Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Katholieke Univ Leuven PSI Leuven Belgium

ISBN: (纸本)9781665445092

Generative adversarial networks (GANs) have shown impressive results in both unconditional and conditional image generation. In recent literature, it is shown that pretrained GANs, on a different dataset, can be transferred to improve the image generation from a small target data. The same, however, has not been well-studied in the case of conditional GANs (cGANs), which provides new opportunities for knowledge transfer compared to unconditional setup. In particular, the new classes may borrow knowledge from the related old classes, or share knowledge among themselves to improve the training. This motivates us to study the problem of efficient conditional GAN transfer with knowledge propagation across classes. To address this problem, we introduce a new GAN transfer method to explicitly propagate the knowledge from the old classes to the new classes. The key idea is to enforce the popularly used conditional batch normalization (BN) to learn the class-specific information of the new classes from that of the old classes, with implicit knowledge sharing among the new ones. This allows for an efficient knowledge propagation from the old classes to the new ones, with the BN parameters increasing linearly with the number of new classes. The extensive evaluation demonstrates the clear superiority of the proposed method over state-of-the-art competitors for efficient conditional GAN transfer tasks. The code is available at: https://***/mshahbazi72/cGANTransfer

关键词： Training computer vision Codes Image synthesis Generative adversarial networks pattern recognition Task analysis

来源：评论

学校读者我要写书评

暂无评论

Deep Implicit Templates for 3D Shape Representation

Deep Implicit Templates for 3D Shape Representation

引用

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

作者： Zheng, Zerong Yu, Tao Dai, Qionghai Liu, Yebin Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781665445092

Deep implicit functions (DIFs), as a kind of 3D shape representation, are becoming more and more popular in the 3D vision community due to their compactness and strong representation power. However, unlike polygon mesh-based templates, it remains a challenge to reason dense correspondences or other semantic relationships across shapes represented by DIFs, which limits its applications in texture transfer, shape analysis and so on. To overcome this limitation and also make DIFs more interpretable, we propose Deep Implicit Templates, a new 3D shape representation that supports explicit correspondence reasoning in deep implicit representations. Our key idea is to formulate DIFs as conditional deformations of a template implicit function. To this end, we propose Spatial Warping LSTM, which decomposes the conditional spatial transformation into multiple point-wise transformations and guarantees generalization capability. Moreover, the training loss is carefully designed in order to achieve high reconstruction accuracy while learning a plausible template with accurate correspondences in an unsupervised manner. Experiments show that our method can not only learn a common implicit template for a collection of shapes, but also establish dense correspondences across all the shapes simultaneously without any supervision.

关键词： Training Geometry computer vision Three-dimensional displays Shape Semantics Network architecture

来源：评论

学校读者我要写书评

暂无评论

Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation

Prototypical Cross-domain Self-supervised Learning for Few-s...

引用

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

作者： Yue, Xiangyu Zheng, Zangwei Zhang, Shanghang Gao, Yang Darrell, Trevor Keutzer, Kurt Vincentelli, Alberto Sangiovanni Univ Calif Berkeley Berkeley CA 94720 USA Nanjing Univ Nanjing Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781665445092

Unsupervised Domain Adaptation (UDA) transfers predictive models from a fully-labeled source domain to an unlabeled target domain. In some applications, however, it is expensive even to collect labels in the source domain, making most previous works impractical. To cope with this problem, recent work performed instance-wise cross-domain self-supervised learning, followed by an additional fine-tuning stage. However, the instance-wise self-supervised learning only learns and aligns low-level discriminative features. In this paper, we propose an endto-end Prototypical Cross-domain Self-Supervised Learning (PCS) framework for Few-shot Unsupervised Domain Adaptation (FUDA)1. PCS not only performs cross-domain low-level feature alignment, but it also encodes and aligns semantic structures in the shared embedding space across domains. Our framework captures category-wise semantic structures of the data by in-domain prototypical contrastive learning;and performs feature alignment through cross-domain prototypical self-supervision. Compared with state-of-the-art methods, PCS improves the mean classification accuracy over different domain pairs on FUDA by 10.5%, 3.5%, 9.0%, and 13.2% on Office, Office-Home, VisDA-2017, and DomainNet, respectively.

关键词： computer vision Semantics Predictive models Benchmark testing pattern recognition

来源：评论

学校读者我要写书评

暂无评论

SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events

SUTD-TrafficQA: A Question Answering Benchmark and an Effici...

引用

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

作者： Xu, Li Huang, He Liu, Jun Singapore Univ Technol & Design Informat Syst Technol & Design Singapore Singapore

ISBN: (纸本)9781665445092

Traffic event cognition and reasoning in videos is an important task that has a wide range of applications in intelligent transportation, assisted driving, and autonomous vehicles. In this paper, we create a novel dataset, SUTD-TrafficQA (Traffic Question Answering), which takes the form of video QA based on the collected 10,080 in-the-wild videos and annotated 62,535 QA pairs, for benchmarking the cognitive capability of causal inference and event understanding models in complex traffic scenarios. Specifically, we propose 6 challenging reasoning tasks corresponding to various traffic scenarios, so as to evaluate the reasoning capability over different kinds of complex yet practical traffic events. Moreover, we propose Eclipse, a novel Efficient glimpse network via dynamic inference, in order to achieve computation-efficient and reliable video reasoning. The experiments show that our method achieves superior performance while reducing the computation cost significantly. The project page: https://github. com/SUTDCV/ SUTD- TrafficQA.

关键词： Transportation Benchmark testing Knowledge discovery Cognition Computational efficiency pattern recognition Reliability

来源：评论

学校读者我要写书评

暂无评论

Real-Time Selfie Video Stabilization

Real-Time Selfie Video Stabilization

引用

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

作者： Yu, Jiyang Ramamoorthi, Ravi Cheng, Keli Sarkis, Michel Bi, Ning Univ Calif San Diego La Jolla CA 92093 USA Qualcomm Technol Inc San Diego CA USA JD AI Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

We propose a novel real-time selfie video stabilization method. Our method is completely automatic and runs at 26 fps. We use a 1D linear convolutional network to directly infer the rigid moving least squares warping which implicitly balances between the global rigidity and local flexibility. Our network structure is specifically designed to stabilize the background and foreground at the same time, while providing optional control of stabilization focus (relative importance of foreground vs. background) to the users. To train our network, we collect a selfie video dataset with 1005 videos, which is significantly larger than previous selfie video datasets. We also propose a grid approximation to the rigid moving least squares that enables the real-time frame warping. Our method is fully automatic and produces visually and quantitatively better results than previous real-time general video stabilization methods. Compared to previous offline selfie video methods, our approach produces comparable quality with a speed improvement of orders of magnitude. Our code and selfie video dataset is available at https://***/jiy173/selfievideostabilization.

关键词： Convolutional codes computer vision Streaming media Real-time systems Rigidity pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Globally Optimal Relative Pose Estimation with Gravity Prior

Globally Optimal Relative Pose Estimation with Gravity Prior

引用

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

作者： Ding, Yaqing Barath, Daniel Yang, Jian Kong, Hui Kukelova, Zuzana Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Czech Tech Univ Fac Elect Engn Visual Recognit Grp Prague Czech Republic SZTAKI MPLab Budapest Hungary Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9781665445092

Smartphones, tablets and camera systems used, e.g., in cars and UAVs, are typically equipped with IMUs (inertial measurement units) that can measure the gravity vector accurately. Using this additional information, the y-axes of the cameras can be aligned, reducing their relative orientation to a single degree-of-freedom. With this assumption, we propose a novel globally optimal solver, minimizing the algebraic error in the least squares sense, to estimate the relative pose in the over-determined case. Based on the epipolar constraint, we convert the optimization problem into solving two polynomials with only two unknowns. Also, a fast solver is proposed using the first-order approximation of the rotation. The proposed solvers are compared with the state-of-the-art ones on four real-world datasets with approx. 50000 image pairs in total. Moreover, we collected a dataset, by a smartphone, consisting of 10933 image pairs, gravity directions and ground truth 3D reconstructions.

关键词： computer vision Measurement units Three-dimensional displays Codes Pose estimation Cameras 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500页 << < 458 459 460 461 462 463 464 465 466 467 > >>

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：