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
23 册 图书

馆藏范围

22,905 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
4,025 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...
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

22,844 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 22906 条记录，以下是4831-4840 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：

Point-NeRF: Point-based Neural Radiance Fields

Point-NeRF: Point-based Neural Radiance Fields

引用

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

作者： Xu, Qiangeng Xu, Zexiang Philip, Julien Bi, Sai Shu, Zhixin Sunkavalli, Kalyan Neumann, Ulrich Univ Southern Calif Los Angeles CA 90089 USA Adobe Res San Jose CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Volumetric neural rendering methods like NeRF [34] generate high-quality view synthesis results but are optimized per-scene leading to prohibitive reconstruction time. On the other hand, deep multi-view stereo methods can quickly reconstruct scene geometry via direct network inference. Point-NeRF combines the advantages of these two approaches by using neural 3D point clouds, with associated neural features, to model a radiance field. Point-NeRF can be rendered efficiently by aggregating neural point features near scene surfaces, in a ray marching-based rendering pipeline. Moreover, Point-NeRF can be initialized via direct inference of a pre-trained deep network to produce a neural point cloud;this point cloud can be finetuned to surpass the visual quality of NeRF with 30x faster training time. Point-NeRF can be combined with other 3D reconstruction methods and handles the errors and outliers in such methods via a novel pruning and growing mechanism.

关键词： Point cloud compression Training Surface reconstruction Solid modeling Three-dimensional displays Pipelines Rendering (computer graphics)

来源：评论

学校读者我要写书评

暂无评论

GIFS: Neural Implicit Function for General Shape Representation

GIFS: Neural Implicit Function for General Shape Representat...

引用

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

作者： Ye, Jianglong Chen, Yuntao Wang, Naiyan Wang, Xiaolong Univ Calif San Diego La Jolla CA 92093 USA TuSimple San Diego CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent development of neural implicit function has shown tremendous success on high-quality 3D shape reconstruction. However, most works divide the space into inside and outside of the shape, which limits their representing power to single-layer and watertight shapes. This limitation leads to tedious data processing (converting non-watertight raw data to watertight) as well as the incapability of representing general object shapes in the real world. In this work, we propose a novel method to represent general shapes including non-watertight shapes and shapes with multi-layer surfaces. We introduce General Implicit Function for 3D Shape (GIFS), which models the relationships between every two points instead of the relationships between points and surfaces. Instead of dividing 3D space into predefined inside-outside regions, GIFS encodes whether two points are separated by any surface. Experiments on ShapeNet show that GIFS outperforms previous state-of-the-art methods in terms of reconstruction quality, rendering efficiency, and visual fidelity. Project page is available at https://***/gifs

关键词： Surface reconstruction Solid modeling Three-dimensional displays Shape Neural networks Visual effects Rendering (computer graphics)

来源：评论

学校读者我要写书评

暂无评论

Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation

Rethinking the Heatmap Regression for Bottom-up Human Pose E...

引用

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

作者： Luo, Zhengxiong Wang, Zhicheng Huang, Yan Wang, Liang Tan, Tieniu Zhou, Erjin Megvii Inc Beijing Peoples R China Univ Chinese Acad Sci UCAS Beijing Peoples R China Natl Lab Pattern Recognit NLPR Ctr Res Intelligent Percept & Comp CRIPAC Beijing Peoples R China Chinese Acad Sci CASIA Inst Automat Beijing Peoples R China MEGVII Res Beijing Peoples R China

ISBN: (纸本)9781665445092

Heatmap regression has become the most prevalent choice for nowadays human pose estimation methods. The ground-truth heatmaps are usually constructed via covering all skeletal keypoints by 2D gaussian kernels. The standard deviations of these kernels are fixed. However, for bottom-up methods, which need to handle a large variance of human scales and labeling ambiguities, the current practice seems unreasonable. To better cope with these problems, we propose the scale-adaptive heatmap regression (SAHR) method, which can adaptively adjust the standard deviation for each keypoint. In this way, SAHR is more tolerant of various human scales and labeling ambiguities. However, SAHR may aggravate the imbalance between fore-background samples, which potentially hurts the improvement of SAHR. Thus, we further introduce the weight-adaptive heatmap regression (WAHR) to help balance the fore-background samples. Extensive experiments show that SAHR together with WAHR largely improves the accuracy of bottom-up human pose estimation. As a result, we finally outperform the state-of-the-art model by +1.5AP and achieve 72.0AP on COCO test-dev2017, which is comparable with the performances of most top-down methods. Source codes are available at https://***/greatlog/SWAHR-HumanPose.

关键词： Heating systems computer vision Codes Computational modeling Pose estimation pattern recognition Labeling

来源：评论

学校读者我要写书评

暂无评论

Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing

Cerberus Transformer: Joint Semantic, Affordance and Attribu...

引用

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

作者： Chen, Xiaoxue Liu, Tianyu Zhao, Hao Zhou, Guyue Zhang, Ya-Qin Tsinghua Univ AIR Beijing Peoples R China HKUST Hong Kong Peoples R China Peking Univ Beijing Peoples R China Intel Labs Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Multi-task indoor scene understanding is widely considered as an intriguing formulation, as the affinity of different tasks may lead to improved performance. In this paper, we tackle the new problem of joint semantic, affordance and attribute parsing. However, successfully resolving it requires a model to capture long-range dependency, learn from weakly aligned data and properly balance sub-tasks during training. To this end, we propose an attention-based architecture named Cerberus and a tailored training framework. Our method effectively addresses aforementioned challenges and achieves state-of-the-art performance on all three tasks. Moreover, an in-depth analysis shows concept affinity consistent with human cognition, which inspires us to explore the possibility of weakly supervised learning. Surprisingly, Cerberus achieves strong results using only 0.1% - 1% annotation. Visualizations further confirm that this success is credited to common attention maps across tasks. Code and models can be accessed at https://***/OPEN-AIR-SUN/Cerberus.

关键词： Training Annotations Affordances Semantics computer architecture Multitasking Transformers

来源：评论

学校读者我要写书评

暂无评论

Hyper-MD: Mesh Denoising with Customized Parameters Aware of Noise Intensity and Geometric Characteristics

Hyper-MD: Mesh Denoising with Customized Parameters Aware of...

引用

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

作者： Wang, Xingtao Wei, Hongliang Fan, Xiaopeng Zhao, Debin Harbin Inst Technol Harbin Peoples R China

ISBN: (纸本)9798350353013;9798350353006

Mesh denoising (MD) is a critical task in geometry processing, as meshes from scanning or AIGC techniques are susceptible to noise contamination. The challenge of MD lies in the diverse nature of mesh facets in terms of geometric characteristics and noise distributions. Despite recent advancements in deep learning-based MD methods, existing MD networks typically neglect the consideration of geometric characteristics and noise distributions. In this paper, we propose Hyper-MD, a hyper-network-based approach that addresses this limitation by dynamically customizing denoising parameters for each facet based on its noise intensity and geometric characteristics. Specifically, HyperMD is composed of a hyper-network and an MD network. For each noisy facet, the hyper-network takes two angles as input to customize parameters for the MD network. These two angles are specially defined to reveal the noise intensity and geometric characteristics of the current facet, respectively. The MD network receives a facet patch as input, and outputs the denoised normal using the customized parameters. Experimental results on synthetic and real-scanned meshes demonstrate that Hyper-MD outperforms state-of-the-art mesh denoising methods.

关键词： computer graphics hyper-network Mesh denoising

来源：评论

学校读者我要写书评

暂无评论

Restore from Restored: Video Restoration with Pseudo Clean Video

Restore from Restored: Video Restoration with Pseudo Clean V...

引用

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

作者： Lee, Seunghwan Cho, Donghyeon Kim, Jiwon Kim, Tae Hyun Hanyang Univ Dept Comp Sci Seoul South Korea Chungnam Natl Univ Dept Elect Engn Daejeon South Korea SKT Vis AI Labs T Brain Seoul South Korea

ISBN: (纸本)9781665445092

In this study, we propose a self-supervised video denoising method called "restore-from-restored." This method fine-tunes a pre-trained network by using a pseudo clean video during the test phase. The pseudo clean video is obtained by applying a noisy video to the baseline network. By adopting a fully convolutional neural network (FCN) as the baseline, we can improve video denoising performance without accurate optical flow estimation and registration steps, in contrast to many conventional video restoration methods, due to the translation equivariant property of the FCN. Specifically, the proposed method can take advantage of plentiful similar patches existing across multiple consecutive frames (i.e., patch-recurrence);these patches can boost the performance of the baseline network by a large margin. We analyze the restoration performance of the fine-tuned video denoising networks with the proposed self-supervision-based learning algorithm, and demonstrate that the FCN can utilize recurring patches without requiring accurate registration among adjacent frames. In our experiments, we apply the proposed method to state-of-the-art denoisers and show that our fine-tuned networks achieve a considerable improvement in denoising performance.

关键词： Training computer vision Noise reduction Estimation Image restoration pattern recognition Noise measurement

来源：评论

学校读者我要写书评

暂无评论

Multi-view Neural Human Rendering

Multi-view Neural Human Rendering

引用

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

作者： Wu, Minye Wang, Yuehao Hu, Qiang Yu, Jingyi ShanghaiTech Univ Shanghai Peoples R China DGene Inc Shanghai Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai Inst Microsyst & Informat Technol Shanghai Peoples R China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

We present an end-to-end Neural Human Renderer (NHR) for dynamic human captures under the multi-view setting. NHR adopts PointNet++ for feature extraction (FE) to enable robust 3D correspondence matching on low quality, dynamic 3D reconstructions. To render new views, we map 3D features onto the target camera as a 2D feature map and employ an anti-aliased CNN to handle holes and noises. Newly synthesized views from NHR can be further used to construct visual hulls to handle textureless and/or dark regions such as black clothing. Comprehensive experiments show NHR significantly outperforms the state-ofthe-art neural and image-based rendering techniques, especially on hands, hair, nose, foot, etc.

关键词： Three-dimensional displays Rendering (computer graphics) Image reconstruction Cameras Feature extraction Geometry Shape

来源：评论

学校读者我要写书评

暂无评论

AdaViT: Adaptive vision Transformers for Efficient Image recognition

AdaViT: Adaptive Vision Transformers for Efficient Image Rec...

引用

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

作者： Meng, Lingchen Li, Hengduo Chen, Bor-Chun Lan, Shiyi Wu, Zuxuan Jiang, Yu-Gang Lim, Ser-Nam Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Info Proc Shanghai Peoples R China Shanghai Collaborat Innovat Ctr Intelligent Visua Shanghai Peoples R China Biren Technol Shanghai Peoples R China Univ Maryland College Pk MD 20742 USA Meta AI Menlo Pk CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Built on top of self-attention mechanisms, vision transformers have demonstrated remarkable performance on a variety of tasks recently. While achieving excellent performance, they still require relatively intensive computational cost that scales up drastically as the numbers of patches, self-attention heads and transformer blocks increase. In this paper, we argue that due to the large variations among images, their need for modeling long-range dependencies between patches differ. To this end, we introduce AdaViT, an adaptive computation framework that learns to derive usage policies on which patches, self-attention heads and transformer blocks to use throughout the backbone on a per-input basis, aiming to improve inference efficiency of vision transformers with a minimal drop of accuracy for image recognition. Optimized jointly with a transformer backbone in an end-to-end manner, a light-weight decision network is attached to the backbone to produce decisions on-the-fly. Extensive experiments on ImageNet demonstrate that our method obtains more than 2x improvement on efficiency compared to state-of-the-art vision transformers with only 0.8% drop of accuracy, achieving good efficiency/accuracy trade-offs conditioned on different computational budgets. We further conduct quantitative and qualitative analysis on learned usage polices and provide more insights on the redundancy in vision transformers. Code is available at https://***/MengLcool/AdaViT.

关键词： computer vision Image recognition Head Law enforcement Computational modeling Redundancy Transformers

来源：评论

学校读者我要写书评

暂无评论

Efficient Feature Transformations for Discriminative and Generative Continual Learning

Efficient Feature Transformations for Discriminative and Gen...

引用

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

作者： Verma, Vinay Kumar Liang, Kevin J. Mehta, Nikhil Rai, Piyush Carin, Lawrence Duke Univ Durham NC 27706 USA Facebook AI Menlo Pk CA USA IIT Kanpur Kanpur Uttar Pradesh India

ISBN: (纸本)9781665445092

As neural networks are increasingly being applied to real-world applications, mechanisms to address distributional shift and sequential task learning without forgetting are critical. Methods incorporating network expansion have shown promise by naturally adding model capacity for learning new tasks while simultaneously avoiding catastrophic forgetting. However, the growth in the number of additional parameters of many of these types of methods can be computationally expensive at larger scales, at times prohibitively so. Instead, we propose a simple task-specific feature map transformation strategy for continual learning, which we call Efficient Feature Transformations (EFTs). These EFTs provide powerful flexibility for learning new tasks, achieved with minimal parameters added to the base architecture. We further propose a feature distance maximization strategy, which significantly improves task prediction in class incremental settings, without needing expensive generative models. We demonstrate the efficacy and efficiency of our method with an extensive set of experiments in discriminative (CIFAR-100 and ImageNet-1K) and generative (LSUN, CUB-200, Cats) sequences of tasks. Even with low single-digit parameter growth rates, EFTs can outperform many other continual learning methods in a wide range of settings.

关键词： Learning systems computer vision Computational modeling Scalability Neural networks Transforms Predictive models

来源：评论

学校读者我要写书评

暂无评论

End-to-end representation learning for Correlation Filter based tracking 30

End-to-end representation learning for Correlation Filter ba...

引用

30th ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Valmadre, Jack Bertinetto, Luca Henriques, Joao Vedaldi, Andrea Torr, Philip H. S. Univ Oxford Oxford England

ISBN: (纸本)9781538604571

The Correlation Filter is an algorithm that trains a linear template to discriminate between images and their translations. It is well suited to object tracking because its formulation in the Fourier domain provides a fast solution, enabling the detector to be re-trained once per frame. Previous works that use the Correlation Filter, however, have adopted features that were either manually designed or trained for a different task. This work is the first to overcome this limitation by interpreting the Correlation Filter learner, which has a closed-form solution, as a differentiable layer in a deep neural network. This enables learning deep features that are tightly coupled to the Correlation Filter. Experiments illustrate that our method has the important practical benefit of allowing lightweight architectures to achieve state-of-the-art performance at high framerates.

关键词： Correlation Training computer architecture Object tracking Filtering algorithms Optimization Target tracking

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 << < 480 481 482 483 484 485 486 487 488 489 > >>

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：